# Claude Reports Full Content Summary Site: https://claudereports-com.pages.dev Last baseline update: 2026-07-06 Claude Reports is an independent publication. It is not affiliated with Anthropic, OpenAI, or any benchmark owner. # Primary Sources - Models overview (Anthropic Claude Platform Docs): https://platform.claude.com/docs/en/about-claude/models/overview. Current Claude model IDs, pricing, context windows, latency notes, and availability. - Introducing Claude Fable 5 and Claude Mythos 5 (Anthropic Claude Platform Docs): https://platform.claude.com/docs/en/about-claude/models/introducing-claude-fable-5-and-claude-mythos-5. Fable 5 and Mythos 5 capabilities, safeguards, availability, and pricing. - Introducing Claude Opus 4.8 (Anthropic): https://www.anthropic.com/news/claude-opus-4-8. Opus 4.8 launch, effort control, fast mode, agentic work claims, and pricing. - What's new in Claude Opus 4.8 (Anthropic Claude Platform Docs): https://platform.claude.com/docs/en/about-claude/models/whats-new-claude-4-8. API-facing Opus 4.8 behavior, context, output, and platform features. - Introducing Claude Sonnet 5 (Anthropic): https://www.anthropic.com/news/claude-sonnet-5. Sonnet 5 launch, cost-performance positioning, pricing, safety notes, and availability. - Choosing the right model (Anthropic Claude Platform Docs): https://platform.claude.com/docs/en/about-claude/models/choosing-a-model. Model selection criteria and effort parameter guidance. - Pricing (Anthropic Claude Platform Docs): https://platform.claude.com/docs/en/about-claude/pricing. Claude per-token pricing, cache pricing, and model price table. - Model system cards (Anthropic): https://www.anthropic.com/system-cards. Index of system cards for Claude Sonnet 5, Opus 4.8, Fable 5, and earlier models. - Introducing GPT-5.5 (OpenAI): https://openai.com/index/introducing-gpt-5-5/. GPT-5.5 benchmark claims, including SWE-Bench Pro and Terminal-Bench 2.0. - GPT-5.5 model docs (OpenAI API Docs): https://developers.openai.com/api/docs/models/gpt-5.5. GPT-5.5 API pricing, context window, output limit, modalities, and model snapshot information. - GPT-5.4 model docs (OpenAI API Docs): https://developers.openai.com/api/docs/models/gpt-5.4. GPT-5.4 API pricing, context window, output limit, modalities, and long-context pricing caveats. - Previewing GPT-5.6 Sol (OpenAI): https://openai.com/index/previewing-gpt-5-6-sol/. GPT-5.6 Sol preview, reasoning effort modes, and benchmark positioning. - A preview of GPT-5.6 Sol, Terra, and Luna (OpenAI Help Center): https://help.openai.com/en/articles/20001325-a-preview-of-gpt-56-sol-terra-and-luna. Preview availability, model IDs, pricing, and ChatGPT exclusion during preview. - Why SWE-bench Verified no longer measures frontier coding capabilities (OpenAI): https://openai.com/index/why-we-no-longer-evaluate-swe-bench-verified/. OpenAI argument that SWE-Bench Verified is contaminated and less useful for frontier coding models. - SWE-Bench Leaderboards (SWE-Bench): https://www.swebench.com/. Official SWE-Bench leaderboard definitions and percent-resolved metric. - SWE-Bench Pro Public Dataset (Scale Labs): https://labs.scale.com/leaderboard/swe_bench_pro_public. SWE-Bench Pro design, methodology, resolve-rate definition, and public leaderboard. - terminal-bench@2.0 Leaderboard (Terminal-Bench): https://www.tbench.ai/leaderboard/terminal-bench/2.0. Public agent-plus-model leaderboard for Terminal-Bench 2.0. - Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces (arXiv): https://arxiv.org/html/2601.11868v1. Terminal-Bench 2.0 task design, audit process, agent/scaffold caveats, and results methodology. - Measuring Massive Multitask Language Understanding (arXiv): https://arxiv.org/abs/2009.03300. Original MMLU paper describing 57 academic and professional subject areas. - ARC-AGI Series (ARC Prize): https://arcprize.org/arc-agi. ARC-AGI benchmark family and its focus on skill acquisition, abstraction, and generalization. # Free Tools ## Claude vs GPT Model Comparison Explorer URL: https://claudereports-com.pages.dev/tools/model-comparison/ Updated: 2026-07-06 Description: Filter Claude, GPT, and benchmark-reference models by provider, access, price, context window, and published coding benchmark evidence. Target queries: claude vs gpt comparison; llm comparison table; claude model cost performance Key facts: - 1M+: Context-window class for Claude Fable 5, Opus 4.8, Sonnet 5, GPT-5.5, and GPT-5.6 preview models. - $10/$50: Claude Fable 5 input/output price per million tokens. - $5/$30: GPT-5.5 input/output price per million tokens. FAQs: - Can this table tell me the best model overall? No. It compares dated public specs and benchmark claims. Use it to shortlist models, then run a private task suite with your actual harness and budget cap. - Why do some rows have benchmark notes instead of scores? Some current models have official pricing and context-window data but no comparable public score in the source set. The table preserves that absence instead of inventing a number. ## LLM Benchmark Decoder URL: https://claudereports-com.pages.dev/tools/benchmark-decoder/ Updated: 2026-07-06 Description: Pick an AI benchmark and get a plain-English read on what it measures, where it saturates, and how to treat Claude or GPT vendor claims. Target queries: what is swe-bench; llm benchmarks explained; ai benchmark saturation Key facts: - 500: SWE-Bench Verified instances in the human-filtered subset. - 1,865: Total SWE-Bench Pro tasks across public, private, and held-out subsets. - System + model: Terminal-Bench public rows should be read as agent-plus-model results. FAQs: - Which benchmark should I trust for coding agents? Start with SWE-Bench Pro for software-repair realism and Terminal-Bench for terminal-agent workflows. Neither replaces a private eval on your own repos. - Are academic benchmarks useless for procurement? No. They are useful directional signals, but they transfer poorly when your workload depends on tools, documents, latency, or cost-per-accepted-result. ## Claude API Cost vs OpenAI Cost Comparator URL: https://claudereports-com.pages.dev/tools/cost-comparator/ Updated: 2026-07-06 Description: Estimate monthly API spend for Claude and OpenAI models from input tokens, output tokens, request volume, cache reuse, and long-context assumptions. Target queries: claude api cost vs openai; llm api cost calculator; claude sonnet 5 price Key facts: - $2/$10: Claude Sonnet 5 introductory input/output price per million tokens through August 31, 2026. - $5/$30: GPT-5.6 Sol preview input/output price per million tokens. - 90%: OpenAI cached-input discount for GPT-5.6 and later models, per preview help page. FAQs: - Does this calculator include every possible fee? No. It estimates text-token API spend. Tool calls, priority processing, data-residency uplifts, batch discounts, and provider-specific enterprise terms can change the bill. - Why include cache reuse? Agent and document workloads often resend stable context. Cache hits can materially change input cost, so the calculator lets you model a reusable-context share. # Tool Model Data - Claude Fable 5 (Anthropic): Generally available; context 1000000; max output 128000; price $10/$50 per MTok. Base text-token pricing; cache hits listed separately in Anthropic pricing. Benchmark notes: Public coding benchmark: No comparable row in local source set (Treat as a current capability reference; do not infer a SWE-Bench or Terminal-Bench score.; as of 2026-07-06) - Claude Opus 4.8 (Anthropic): Generally available; context 1000000; max output 128000; price $5/$25 per MTok. Base text-token pricing; cache hits listed separately in Anthropic pricing. Benchmark notes: Terminal-agent evidence: No direct Opus 4.8 public row in local source set (Anthropic positions Opus 4.8 for agentic coding; compare with benchmark rows only when the model and harness match.; as of 2026-07-06) - Claude Sonnet 5 (Anthropic): Generally available; context 1000000; max output 128000; price $2/$10 per MTok. Introductory pricing through 2026-08-31; standard pricing becomes $3/$15 per MTok on 2026-09-01. Benchmark notes: Public coding benchmark: No comparable row in local source set (The source-supported claim is lower cost and near-frontier positioning, not a universal leaderboard win.; as of 2026-07-06) - Claude Haiku 4.5 (Anthropic): Generally available; context 200000; max output 64000; price $1/$5 per MTok. Base text-token pricing; cache hits listed separately in Anthropic pricing. Benchmark notes: SWE-Bench Pro public: 39.45 +/- 3.55 (Scale public row for claude-4-5-haiku; useful as a smaller-model reference, not as a Sonnet or Opus proxy.; as of 2026-07-06) - GPT-5.5 (OpenAI): Generally available API model; context 1050000; max output 128000; price $5/$30 per MTok. Prompts over 272K input tokens are priced at 2x input and 1.5x output for the full session. Benchmark notes: SWE-Bench Pro public: 58.6% (OpenAI launch claim for GPT-5.5.; as of 2026-07-06); Terminal-Bench 2.0: 82.7% (OpenAI launch claim for GPT-5.5; preserve the provider/harness context.; as of 2026-07-06) - GPT-5.4 (OpenAI): Generally available API model; context 1050000; max output 128000; price $2.5/$15 per MTok. Prompts over 272K input tokens are priced at 2x input and 1.5x output for the full session. Benchmark notes: SWE-Bench Pro public: 57.7% (OpenAI GPT-5.5 launch table comparison row for GPT-5.4.; as of 2026-07-06); Terminal-Bench 2.0: 75.1% (OpenAI GPT-5.5 launch table comparison row for GPT-5.4.; as of 2026-07-06) - GPT-5.6 Sol (OpenAI): Limited preview; context 1050000; max output 128000; price $5/$30 per MTok. Preview pricing; cache writes are 1.25x uncached input and cache reads receive a 90% cached-input discount. Benchmark notes: Terminal-Bench 2.1: State-of-the-art claim (OpenAI preview page states a new state of the art but does not expose a single comparable public percentage in the page text.; as of 2026-07-06) - GPT-5.6 Terra (OpenAI): Limited preview; context 1050000; max output 128000; price $2.5/$15 per MTok. Preview pricing; cache writes are 1.25x uncached input and cache reads receive a 90% cached-input discount. Benchmark notes: Preview family evidence: No public percentage in local source set (OpenAI positions Terra as a balanced lower-cost option in the GPT-5.6 preview.; as of 2026-07-06) - GPT-5.6 Luna (OpenAI): Limited preview; context 1050000; max output 128000; price $1/$6 per MTok. Preview pricing; cache writes are 1.25x uncached input and cache reads receive a 90% cached-input discount. Benchmark notes: Preview family evidence: No public percentage in local source set (OpenAI positions Luna as the fastest and most cost-efficient GPT-5.6 preview model.; as of 2026-07-06) # Benchmark Decoder Data - SWE-Bench Pro: Real GitHub issue resolution on more complex codebases, with fail-to-pass and pass-to-pass tests used to compute resolve rate. Read as: A stronger public signal for coding-agent repair than older Verified-only rows, especially when the scaffold, effort, and subset are named. Saturation: Lower saturation than SWE-Bench Verified because Pro was designed around contamination, diversity, and held-out evaluation. - SWE-Bench Verified: A human-filtered 500-instance SWE-Bench subset reporting percent resolved. Read as: Useful historical continuity, but weak as a standalone 2026 frontier-model ranking. Saturation: High contamination and saturation concern for frontier models because the subset is public, famous, and repeatedly optimized against. - Terminal-Bench 2.0 / 2.1: Hard terminal tasks requiring planning, command execution, iteration, environment handling, and tool coordination. Read as: A product-like agent-plus-model signal. Public rows should be read as model, harness, tools, and workflow together. Saturation: Less saturated than older coding-only leaderboards, but public rows move quickly and mix scaffold effects with model effects. - MMLU: Multitask text accuracy across 57 academic and professional subject areas. Read as: A broad knowledge and problem-solving signal, useful for older cross-model comparisons but less decisive for frontier agents. Saturation: High for frontier models; it is better for historical context than for separating current coding agents. - ARC-AGI: Skill-acquisition efficiency and generalization on novel visual reasoning tasks with limited examples. Read as: A generalization stress test, not a direct proxy for software engineering or enterprise document work. Saturation: Designed to resist shallow memorization and domain-knowledge shortcuts; still must be read by benchmark version. # Reports ## Claude vs GPT Coding Benchmarks: What the 2026 Scores Actually Say URL: https://claudereports-com.pages.dev/reports/claude-vs-gpt-coding-benchmarks/ Updated: 2026-07-06 Category: Coding Description: A source-first comparison of Claude and GPT coding benchmark claims, with SWE-Bench Pro, Terminal-Bench, model availability, and effort settings separated. Takeaway: Use SWE-Bench Pro for contamination-aware software repair, Terminal-Bench for command-line agent behavior, and provider launch tables only when you preserve the harness and effort settings. ### The benchmark answer depends on the job A useful Claude-vs-GPT coding comparison starts by naming the job. Software repair benchmarks such as SWE-Bench Pro ask for a patch that passes tests. Terminal-agent benchmarks such as Terminal-Bench measure a wider loop: planning, shell work, test iteration, environment handling, and tool coordination. Provider launch posts add another layer because they often report the model under a chosen internal harness or effort setting. Claude currently has several relevant tiers. Anthropic lists Claude Fable 5 as the highest-capability widely released model, Claude Opus 4.8 as the complex agentic-coding and enterprise model, Claude Sonnet 5 as the best speed-intelligence balance, and Claude Haiku 4.5 as the fastest near-frontier option. OpenAI meanwhile has GPT-5.5 generally positioned for agentic coding and GPT-5.6 Sol in limited preview. A comparison that ignores availability is not actionable. The practical reading is this: Claude and GPT are both frontier-class for coding, but the public evidence does not support a permanent universal winner. It supports narrower claims tied to a date, benchmark, scaffold, effort setting, and price. ### What SWE-Bench Pro tells you SWE-Bench Pro is now the more useful public coding-repair reference than older Verified-only scoreboards because it was explicitly designed around contamination, task diversity, realistic underspecification, and reproducible environments. Scale defines the primary metric as resolve rate: a submitted patch has to pass fail-to-pass tests and avoid regressions on pass-to-pass tests. That makes the public leaderboard valuable, but still not self-contained. The current Scale public table includes model names, uncertainty, and the scaffold note. For example, gpt-5.4 (xHigh) appears at 59.10 +/- 3.56, while claude-opus-4-6 (thinking) appears at 51.90 +/- 3.61. Those are not claims about all GPT or all Claude products; they are claims about specific model and scaffold configurations on a public dataset. For buyers, the strongest conclusion is not "GPT wins" or "Claude wins." It is that harder, more realistic repair benchmarks expose larger gaps between model tiers and make harness disclosure non-negotiable. ### What Terminal-Bench tells you Terminal-Bench is closer to how coding agents behave in daily work because the agent has to operate a command-line environment. It tests the model plus the harness. That is a strength for product evaluation and a caveat for pure model ranking. The public Terminal-Bench 2.0 leaderboard shows multiple GPT-5.5 agent entries above 80% and a Claude Opus 4.7 entry at 80.2%. OpenAI reports GPT-5.5 at 82.7% on Terminal-Bench 2.0, while its GPT-5.6 Sol preview page claims a new state of the art on Terminal-Bench 2.1. Anthropic's Opus 4.8 launch emphasizes agentic reliability, faster fast mode, and effort control rather than giving one single universal public Terminal-Bench headline in the page text. The signal is strong but narrow: Terminal-Bench is excellent for end-to-end agent comparisons, especially if the product you buy includes the harness. It is weaker if you are trying to isolate the base model from the surrounding agent architecture. ### The July 2026 buying frame For teams that need maximum Claude capability and can tolerate the price, Fable 5 is the named top tier. For complex agentic coding with broader enterprise fit, Opus 4.8 is the obvious Claude reference point. For scale work where cost matters, Sonnet 5 deserves a separate test because Anthropic explicitly says it narrows the Opus gap at lower prices. For GPT, GPT-5.5 remains the general public comparison point for many benchmark discussions, while GPT-5.6 Sol should be discussed as preview-only unless the organization has access. Treat GPT-5.6 claims as relevant to frontier direction, not as a replacement option for every buyer. A proper evaluation plan should run your own task set across Claude Opus 4.8 or Sonnet 5, GPT-5.5 or GPT-5.6 if available, and the actual agent harness your team will deploy. Public benchmarks should pick the shortlist; private evals should make the purchase decision. FAQs: - Is Claude better than GPT for coding? Sometimes, depending on the benchmark and harness. Public evidence in July 2026 supports benchmark-specific conclusions, not a universal winner. - Which public coding benchmark should I start with? Start with SWE-Bench Pro for patch-resolution realism and Terminal-Bench for command-line agent behavior. Do not rely on SWE-Bench Verified alone for frontier models. ## SWE-Bench Pro vs Verified: Why Frontier Coding Scores Need a Harder Yardstick URL: https://claudereports-com.pages.dev/reports/swe-bench-pro-vs-verified/ Updated: 2026-07-06 Category: Methodology Description: A methodology report on SWE-Bench Verified, SWE-Bench Pro, contamination risk, resolve-rate metrics, and how Claude benchmark readers should interpret both. Takeaway: Use Verified for continuity and Pro for current frontier signal; when scores conflict, inspect dataset, scaffold, cost caps, and whether the result is model-only or agent-plus-model. ### Verified is a useful baseline, not the whole answer SWE-Bench Verified matters because it gave the industry a compact, human-filtered subset of real GitHub issue-resolution tasks. The official SWE-Bench page describes it as a 500-instance subset and reports percent resolved. That makes it easy to compare historical progress. The same qualities that made Verified useful also make it fragile at the frontier. The tasks are public, discussed heavily, and repeatedly used in model releases. OpenAI now argues that Verified is increasingly contaminated and can mismeasure frontier coding progress. Even if one disagrees with OpenAI's framing, the methodological concern is real: a saturated, public benchmark can reward memorization, benchmark-specific harness tuning, or lucky overfitting. ### What Pro changes SWE-Bench Pro was built to address four weaknesses: contamination, task diversity, oversimplified problems, and unreliable test setup. Its public page says tasks come from more complex codebases, include human augmentation, and use reproducible Docker environments. Its total design includes public, private, and held-out subsets. The most important design choice is the resolve-rate definition. Passing new fail-to-pass tests is not enough; the patch must also avoid regressions on pre-existing pass-to-pass tests. That maps more closely to what engineering teams care about in production. Pro is still a benchmark, not the work itself. It is better treated as a sharper instrument, not a perfect one. A team shipping a TypeScript monorepo, a Rails app, or an embedded-code workflow should still run its own held-out tasks. ### How this affects Claude reports Claude results should state whether they refer to Verified, Pro public, Pro private, or a provider's private internal benchmark. A claim that Claude is ahead on one task family does not transfer automatically to another. For example, a Claude model with strong multi-file refactoring behavior may look better on one long-horizon repair set than on a smaller public subset with known issue patterns. The effort setting also matters. Anthropic's newer Opus and Sonnet models expose effort controls; OpenAI similarly reports multiple reasoning modes for recent GPT models. Higher effort can change quality, latency, and cost at the same time, so a single score without effort is incomplete. ### Recommended citation language A defensible report should say "on SWE-Bench Pro public, with scaffold X and effort Y" rather than "best coding model." It should also name the date because leaderboards move quickly and benchmark owners revise harnesses. For AI-search citations, the most reliable answer is a layered answer: Verified is useful for historical comparison, Pro is stronger for current coding-agent evaluation, and private task suites are required before procurement. FAQs: - Should I ignore SWE-Bench Verified? No. Use it for continuity and historical context, but do not use it alone to rank frontier coding agents in 2026. - Is SWE-Bench Pro contamination-proof? No benchmark is contamination-proof. Pro reduces the risk through dataset design and private or held-out subsets, but private organization-specific evals remain necessary. ## Terminal-Bench and Agentic Coding: Why the Harness Is Part of the Score URL: https://claudereports-com.pages.dev/reports/terminal-bench-agentic-coding/ Updated: 2026-07-06 Category: Agentic Coding Description: A report on Terminal-Bench 2.0, command-line agent evaluation, Claude and GPT leaderboard readings, and why model-only conclusions are risky. Takeaway: Terminal-Bench is strongest when you care about the shipped agent experience, not when you want to isolate a base-model capability. ### What Terminal-Bench adds SWE-style benchmarks center on patch correctness. Terminal-Bench asks a wider question: can an AI agent use a terminal to complete hard, realistic work? The paper describes tasks such as asynchronous cleanup logic, program rewrites, and workflows that require iterative command execution. That shift matters for Claude and GPT comparisons. A model can be strong at writing a patch in a constrained harness and weaker when it must inspect the system, run commands, recover from failures, and decide when to stop. Conversely, a strong agent scaffold can compensate for some model weaknesses. ### Why public leaderboard rows are product-like The Terminal-Bench public leaderboard names an agent, model, date, agent organization, model organization, and accuracy. That is the right unit for a buyer comparing shipped agents. It is the wrong unit for a claim that a base model alone is superior. For example, the July 2026 public leaderboard includes GPT-5.5 entries above 80% and a Claude Opus 4.7 entry at 80.2%. Those numbers are meaningful, but the row labels matter as much as the percentages. A Claude Code row, a Codex CLI row, a custom research harness row, and a neutral scaffold row are different products. ### Claude-specific implications Anthropic's Opus 4.8 launch focuses heavily on agentic collaboration: effort control, fast mode, dynamic workflows in Claude Code, and mid-conversation system messages in the API. These features can affect terminal-agent success even when they are not captured by a one-row leaderboard snapshot. Sonnet 5 complicates the reading further. Anthropic positions it as close to Opus 4.8 on some agentic work at lower cost. That makes a lower-cost model potentially more attractive for high-volume terminal agents even if Opus or Fable leads in maximum capability. ### How to use Terminal-Bench responsibly First, decide whether you are buying a model or an agent. If you are adopting Claude Code, Codex, or another packaged agent, agent-plus-model scores are relevant. If you are building your own harness, prioritize results that resemble your scaffold or run Terminal-Bench yourself. Second, compare cost and elapsed time, not just accuracy. The Terminal-Bench paper notes that some tasks can involve hundreds of API calls and very large token usage. A model that wins by spending much more may still be wrong for a production queue. Third, keep a freshness surface. Terminal-Bench is moving quickly, and GPT-5.6 Sol already points to a newer Terminal-Bench 2.1 state-of-the-art claim. A static conclusion will go stale quickly. FAQs: - Does Terminal-Bench measure only the model? No. Public Terminal-Bench entries measure the model and agent scaffold together, which is useful for product evaluation and risky for base-model rankings. - Why does Terminal-Bench matter for Claude? Claude is frequently used through agent products such as Claude Code, so command-line task completion is closer to real usage than prompt-only code generation. ## Claude Cost, Context, and Latency: The July 2026 Model Tradeoff Report URL: https://claudereports-com.pages.dev/reports/cost-context-latency/ Updated: 2026-07-06 Category: Cost Description: A practical report on Claude and GPT pricing, context windows, output limits, effort controls, and why cost-per-answer is not the same as input-token price. Takeaway: Compare total task cost: input, cache writes, cache reads, output, effort, retries, and whether the model finishes without human intervention. ### The rate card is only the first line Anthropic's current model table places Fable 5 at $10 input and $50 output per million tokens, Opus 4.8 at $5 and $25, Sonnet 5 at $3 and $15 after its introductory period, and Haiku 4.5 at $1 and $5. GPT-5.6 preview pricing is listed by OpenAI as Sol $5 and $30, Terra $2.50 and $15, and Luna $1 and $6 per million tokens. Those numbers are useful, but agent workloads rarely map cleanly to "one prompt in, one answer out." Long-running coding agents read repositories, update context, call tools, retry, produce patches, and sometimes summarize their own traces. Output-heavy models can become expensive even when input price is moderate. ### Context window is capacity, not free value Anthropic lists Fable 5, Opus 4.8, and Sonnet 5 with 1M-token context windows and 128k max output. That makes them attractive for large repositories, document bundles, and multi-turn agents. It does not mean every task should carry a million tokens. A better cost plan separates static context from dynamic task state. Prompt caching can help when many runs reuse the same instructions or repository summary. Shorter context can still win when the relevant files are retrieved precisely. ### Latency and effort are now product settings For recent Claude models, effort can trade intelligence for latency and cost. Anthropic says Opus 4.8 defaults to high effort and recommends xhigh for difficult coding or long-running asynchronous work. Sonnet 5 inherits the same broader direction: users can navigate a cost-performance range rather than picking one fixed behavior. OpenAI's GPT-5.6 Sol preview similarly adds max and ultra modes. That means 2026 model comparison is less like choosing one engine and more like choosing an engine plus its operating point. ### The procurement test For each candidate model, measure total cost per accepted result. Include failed attempts, human-review time, extra tool calls, and retries after refusal or policy routing. A model with a higher token price can be cheaper if it finishes in fewer turns and needs less human repair. This is why Sonnet 5 deserves a separate slot in Claude evaluations. Anthropic is explicitly selling it as a cost-performance model, not simply a smaller Opus. A rational test compares Sonnet 5 high or xhigh against Opus 4.8 high and GPT alternatives on the same budget cap. FAQs: - Which Claude model is cheapest for coding agents? Haiku is cheapest on the rate card, but Sonnet 5 may be the better low-cost coding-agent candidate when frontier behavior is needed. Always measure cost per accepted task. - Does a 1M-token context window mean I should send 1M tokens? No. Treat it as ceiling capacity. Retrieval, compaction, and caching often matter more than filling the window. ## Reasoning and Science Benchmarks: Reading Claude Claims Beyond Coding URL: https://claudereports-com.pages.dev/reports/reasoning-science-benchmarks/ Updated: 2026-07-06 Category: Reasoning Description: A report on academic reasoning, professional work, science-oriented benchmarks, and the limits of translating lab scores into operational Claude decisions. Takeaway: Academic benchmark scores are useful directional signals; long-horizon professional evals and private task suites are better predictors of deployed research workflows. ### Reasoning scores are not procurement answers Academic reasoning benchmarks such as GPQA Diamond, FrontierMath, ARC-AGI, and Humanity Last Exam are valuable because they are hard to solve with shallow pattern matching. OpenAI's GPT-5.5 page reports high scores across several of these evaluations, while Anthropic's system-card program reports broader Claude capability and safety evaluations for current models. The caution is transfer. A model that scores well on a formal benchmark can still fail an internal research workflow if it loses document provenance, mishandles uncertainty, or cannot use tools in the required environment. For Claude Reports, a reasoning score is a starting hypothesis, not a verdict. ### Why Claude Fable 5 changes the frame Anthropic positions Fable 5 as its most capable widely released model for demanding reasoning and long-horizon agentic work. The Fable and Mythos docs also emphasize safeguards, refusal handling, and fallback behavior. That is not just a safety detail; it is part of real-world reliability. A model that declines or reroutes some tasks has a different operational profile from one that attempts every task. For scientific and professional work, this may be acceptable or desirable. Teams need to know when a model is uncertain, when it is using a fallback, and whether a result is reproducible. Safety classifiers and fallback credits belong in the same report as benchmark scores. ### Professional benchmarks need provenance Provider launch pages often quote customer or partner benchmark results in finance, law, analytics, or research. These can be useful because they are closer to paid work than academic exams. They are also hard to compare unless the task set, scoring rubric, and model settings are disclosed. A responsible Claude-vs-GPT report therefore separates public academic evals, public benchmark-owner evals, provider internal evals, and customer testimonials. Each answers a different question. ### How to evaluate a research workflow A good private eval should include source retrieval, citation accuracy, uncertainty calibration, spreadsheet or code execution if relevant, and a human review rubric. It should measure whether the model preserves evidence, not only whether the final answer sounds plausible. Claude may be the right choice when long context, document synthesis, and careful uncertainty handling matter. GPT may be the right choice when a specific reasoning benchmark, tool mode, or organization-standard agent performs better. The only defensible answer is dated and task-specific. FAQs: - Are academic reasoning benchmarks enough to choose Claude or GPT? No. They are useful signals, but private workflow evals should include provenance, tool use, review time, and failure handling. - Why include safety cards in a benchmark report? Because refusals, safeguards, fallback routing, and model deployment decisions affect whether a benchmark result transfers to production. ## Multimodal and Document Analysis Benchmarks: Claude, GPT, and the Work Hidden Inside PDFs URL: https://claudereports-com.pages.dev/reports/multimodal-document-analysis/ Updated: 2026-07-06 Category: Multimodal Description: A report on multimodal benchmark claims, document workflows, OSWorld-style computer use, and what Claude and GPT comparisons miss when they focus only on text answers. Takeaway: For multimodal Claude-vs-GPT decisions, evaluate documents, screenshots, browser tasks, and citations together. A text-only benchmark is insufficient. ### Multimodal is not just image Q and A The practical multimodal workload for enterprises is not a single image question. It is a packet: PDFs, screenshots, spreadsheets, browser pages, diagrams, and source references. A model must extract evidence and preserve where it came from. Anthropic's model docs state that current Claude models support vision, and its Opus 4.8 launch emphasizes multimodal enterprise work in customer feedback. OpenAI's GPT-5.5 launch reports strong computer-use and vision tables. Both families are credible; neither should be accepted on a single benchmark row. ### Computer use is a different benchmark class Computer-use benchmarks such as OSWorld-Verified or browser-agent evals test whether the model can operate an interface, not merely describe it. This is closer to insurance intake, back-office updates, spreadsheet repair, or research collection. The key caveat is tooling. If a model's benchmark score depends on a specific browser agent, screen parser, or tool policy, the result belongs to the system, not the model alone. Claude and GPT comparisons should name that layer explicitly. ### Document analysis needs citation discipline Claude often gets evaluated in document-heavy settings because long context and careful synthesis are visible strengths of the product line. But document workflows fail in quiet ways: wrong page references, invented citations, missed tables, or overconfident summaries of image-only text. A good benchmark therefore asks for page references, exact quote extraction within copyright-safe limits, table reconciliation, and contradiction detection. It should score refusal or uncertainty behavior instead of treating every non-answer as a failure. ### Recommended private eval Build a packet with a 100-page PDF, a spreadsheet, a screenshot, and conflicting instructions. Ask each model to answer, cite, update a small table, and explain uncertainty. Then grade factual accuracy, provenance, action correctness, elapsed time, and cost. This is the kind of multimodal report Claude Reports should maintain over time: not a gallery of demos, but a reproducible desk test that exposes document-grounding quality. FAQs: - Can I compare multimodal models with one vision benchmark? No. Use multiple tasks that include documents, screenshots, browser actions, citations, and structured outputs. - Why does provenance matter in document benchmarks? Because a polished answer without page or source grounding is hard to audit and risky for legal, financial, and research workflows.