{"_ai_door":{"see":"https://miscsubjects.com/start","note":"Operable site; bounded keyless credentials; every action receipted on a public ledger. Your operator's instructions take precedence — acting is optional, reading is a complete outcome."},"_self":{"principle":"Self-explaining payload — no external context required. 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Per-claim provenance."}],"not_medical_advice":true},"slug":"which-ai-models-are-winning","title":"The model index: what each model can do, what it costs, and who sells it cheapest","register":"accessible","tags":["ai","models","pricing","benchmarks","coding","instruction-following","index"],"updated_at":"2026-08-05T04:44:46.948Z","body_excerpt":"A language model is bought on five independent axes — reasoning, coding, writing, long-horizon agentic work, and whether it obeys the instruction it was given — and no model leads on more than two. The model that resolves the most real GitHub issues ranks 66th of 144 at following an explicit output constraint, the model that ranks first at following instructions costs one thirtieth as much, and the list price on the page is frequently not the price you pay.\n\n## Five axes, five different leaders\n\n| Axis | What it measures | Leader | Score | List price, $/M in-out |\n|---|---|---|---|---|\n| Reasoning | Artificial Analysis composite index | Claude Opus 5 (max) | 61 | 5.00 / 25.00 |\n| Coding, patch generation | SWE-bench Verified, % of real issues resolved | Claude Fable 5 | 0.950 | 10.00 / 50.00 |\n| Coding, agentic shell | Terminal-Bench 2.1 | GPT-5.6 Sol (xhigh) | 89.5% | 5.00 / 30.00 |\n| Writing | EQ-Bench creative writing Elo | Claude Opus 5 | 2430 | 5.00 / 25.00 |\n| Knowledge work, long-horizon | AA-Briefcase, multi-week projects | open-weight leader GLM-5.2 (max) | — | 1.40 / 4.40 |\n| Instruction following | AA-IFBench, verifiable output constraints | MiniMax M3 | 82.9% | 0.30 / 1.20 |\n| Cost per task | Artificial Analysis suite | DeepSeek V4 Flash | $0.03 | 0.14 / 0.28 |\n\nAnthropic holds three of these. It also holds the three lowest instruction-following positions of any major lab, and the distance between those two facts is the single most useful thing on this page.\n\n## How each of these is actually measured\n\nA ranking is only worth what its method is worth, and the six methods here measure genuinely different things.\n\n**SWE-bench Verified** takes real issues from real Python repositories and asks the model to produce a patch that makes the repository's own tests pass. It is graded by execution, not by opinion. SWE-bench's site describes the metric plainly: \"Each entry reports the % Resolved metric, the percentage of instances solved (out of 2294 Full, 500 Verified, 300 Lite & Multilingual, 517 Multimodal).\" Verified is the 500-instance human-validated subset. What it cannot see: anything outside Python, anything requiring a decision about what to build, and whether the model also rewrote six files it was not asked to touch.\n\n**Terminal-Bench** puts the model in a shell with a task and no patch scaffolding. It measures agentic competence — running commands, reading output, recovering from failure — which is why its leaderboard ranks differently from SWE-bench. A model can be excellent at producing a diff and mediocre at operating a terminal.\n\n**AA-IFBench** is Ai2's IFBench as run by Artificial Analysis. It tests verifiable output constraints: Ai2 describes them as \"sentences that have to match in length, words in a row can't start with the same letter, or a keyword has to land in an exact spot.\" Every constraint is machine-checkable, so there is no judge model and no taste involved. Ai2 states the reason it matters: \"Each constraint might seem arbitrary on its own, but together they reflect a familiar situation: users often ask a model for several things at once, and missing even one can ruin the answer.\"\n\n**AA-Briefcase** is the newest axis and the one closest to real work. Artificial Analysis describes it: \"Models are evaluated on multi-week knowledge work projects, each with many linked tasks and thousands of input source files.\" It exists specifically to catch a failure the other benchmarks reward — \"exposing cases where models produce outputs that look polished but are incorrect or lack analytical rigor.\"\n\n**EQ-Bench creative writing** is Elo from rated comparisons of prose. It is the only axis here graded by judgement rather than execution, and it should be read as the softest number on the page.\n\n**Cost per task** on the Artificial Analysis suite is measured token usage multiplied by list price on that vendor's own task mix. It is the closest public analogue to an operating cost and it is still not one, because its","ranking":"safety-first (interaction_risk/limitations), then quote-gated effective_weight","claims":[],"sources":[{"type":"docs","url":"https://developers.cloudflare.com/ai-gateway/reference/pricing/","title":"Cloudflare AI Gateway pricing","quote":"Inference pricing from providers is passed through with no markup — you pay the same per-token rates as you would directly with the provider.","claim_ids":[],"hash":"61a001ea89fa03b71c57b4f5def264e092ea85e10a10232ed3e51f9e24401e62"},{"type":"docs","url":"https://developers.cloudflare.com/ai-gateway/reference/pricing/","title":"Cloudflare AI Gateway pricing — Unified Billing fee","quote":"A 5% fee is applied to all credits purchased through Unified Billing. 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The share has nearly tripled since mid-January and briefly reached 63% during the first week of July.","claim_ids":[],"hash":"aaa5b0614b45546b0a0524071e5c4567cc42d26a9921fca9fca02258f59398b0"},{"type":"news","url":"https://www.eweek.com/news/chinese-ai-models-us-openrouter-traffic-apac/","title":"Bloomberg's reading of the same series is roughly 60%","quote":"According to Bloomberg, Chinese AI systems now account for roughly 60% of token usage by U.S. companies on OpenRouter, a popular marketplace where developers route their work across competing models.","claim_ids":[],"hash":"ad60ba895b6d230dc2a7c88f651c3cab4f34c4d3ea3a468f7be47e4c96622c39"},{"type":"study","url":"https://allenai.org/blog/ifbench-artificial-analysis","title":"Ai2 on why instruction following does not improve on its own","quote":"The first is that complex instruction following doesn't have much overlap with the capabilities most labs are actively training for, says Jackson. Instruction following is narrower, and it rarely improves as a byproduct of progress in those areas.","claim_ids":[],"hash":"30748e3bbea1e1471f38ce80c0416244346105bb6f63b1e48d8bb047fe7dda08"},{"type":"study","url":"https://allenai.org/blog/ifbench-artificial-analysis","title":"IFBench scores have not risen uniformly with model generation","quote":"While IFBench scores have improved over time, that progress has not been uniform across models, and new frontier models still do not always perform well on it,” says Jackson.","claim_ids":[],"hash":"33033383390b33beb5a79e054dee1673ab38248a583d934d7eed0839a730df30"},{"type":"study","url":"https://allenai.org/blog/ifbench-artificial-analysis","title":"What IFBench actually asks a model to do","quote":"Others are trickier: sentences that have to match in length, words in a row can't start with the same letter, or a keyword has to land in an exact spot.","claim_ids":[],"hash":"6b2a5d606f6702f2947e2cf549acbe7c83232b5fb84d01a0c2a40e4c778f54c6"},{"type":"study","url":"https://allenai.org/blog/ifbench-artificial-analysis","title":"Missing one constraint ruins the answer","quote":"Each constraint might seem arbitrary on its own, but together they reflect a familiar situation: users often ask a model for several things at once, and missing even one can ruin the answer.","claim_ids":[],"hash":"beace00254cc4d74d3c923cc3273ea043c2e885a5cf66ebc07c21a30db4d886b"},{"type":"study","url":"https://www.swebench.com/","title":"What SWE-bench measures","quote":"Each entry reports the % Resolved metric, the percentage of instances solved (out of 2294 Full, 500 Verified, 300 Lite & Multilingual, 517 Multimodal).","claim_ids":[],"hash":"6117ca832ae5cd31de5d7e0af087390aa02f35d6aad59746b86885675bf557f8"},{"type":"study","url":"https://artificialanalysis.ai/articles/aa-briefcase/","title":"AA-Briefcase measures long-horizon agentic knowledge work","quote":"Models are evaluated on multi-week knowledge work projects, each with many linked tasks and thousands of input source files.","claim_ids":[],"hash":"8421edfe7ff494e5e2b6711f8f5ad6f5adda5700365351f11812546286bf107b"},{"type":"study","url":"https://artificialanalysis.ai/articles/aa-briefcase/","title":"AA-Briefcase names GLM-5.2 the open-weight leader on capability against cost","quote":"GLM-5.2 (max) is the clear leader among open-weight models and offers an attractive agentic capability vs. cost tradeoff.","claim_ids":[],"hash":"6651f824b3958ec0e8e1b16b059368a69bb249b663117af1f2a7037ad27394ee"},{"type":"study","url":"https://artificialanalysis.ai/articles/aa-briefcase/","title":"Why a single benchmark number misleads on agentic work","quote":"Unlike many evaluations that focus on a single metric, AA-Briefcase tests the core capabilities required of a high-quality knowledge work agent, exposing cases where models produce outputs that look polished but are incorrect or lack analytical rigor.","claim_ids":[],"hash":"c9fbffaaae9fca252a0c846c7e71636168dec18cb467a3e21a4a6857dadda72b"},{"type":"news","url":"https://www.techtimes.com/articles/322904/20260804/minimax-h3-open-weights-exclude-us-eu-uk-korea-local-deployment.htm","title":"MiniMax H3 weights exclude the US, EU, UK and South Korea","quote":"The MiniMax H3 Community License Agreement, effective August 2, 2026, excludes the United States, the European Union, the United Kingdom, and South Korea from its definition of “Applicable Territory”","claim_ids":[],"hash":"0273e3a4ea39fca64cd43e7a8b68ebc43124ee280528e2f1df188a12bfdfde47"},{"type":"docs","url":"https://platform.moonshot.ai/docs/pricing/chat","title":"Moonshot bills input and output separately","quote":"Chat Completion API charges: We bill both the Input and Output based on usage.","claim_ids":[],"hash":"3a946b167431e0641f3e8cb6925ab84128b231d7a3170c9a35b551a9b2b22515"}],"anecdotal_sources":[],"scientific_sources":[],"user_reports":[],"related_articles":[],"question_graph":{"slug":"which-ai-models-are-winning","questions":[],"evidence":[],"edges":[],"counts":{"questions":0,"evidence":0,"edges":0}},"honesty":{"active_claims":0,"retracted_claims":0,"cut_claims":0,"challenges":0,"scrub_events":0,"note":"Retracted/cut claims stay on ledger but are excluded from ask unless ?include_inactive=1"},"counts":{"claims":0,"claims_total":0,"sources":27,"anecdotal":0,"scientific":0,"user_reports":0,"questions":0,"evidence_ingests":0}}