The model index: what each model can do, what it costs, and who sells it cheapest

Five axes, five different leaders

Anthropic 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.

How each of these is actually measured

A ranking is only worth what its method is worth, and the six methods here measure genuinely different things.

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.

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.

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."

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."

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.

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 task mix is not yours.

Why the obedience axis is the one that lags

This is the mechanism, and Ai2 states it directly rather than leaving it to inference. On why instruction following does not rise with each model generation: "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."

And on whether the newest models fixed it: "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.

That is the whole explanation for the table below. Obedience is not a capability that arrives free with scale. It is a separate training objective, and a lab optimising for reasoning, coding and helpfulness can ship a generational leap on all three while going sideways or backwards on it.

The instruction-following table

AA-IFBench as mirrored by BenchLM, data verified 4 August 2026, 144 models scored.

Claude Fable 5 is first in the world at resolving real GitHub issues and 66th at holding an output constraint. Claude Sonnet 4.6 sits 104th, below open models a fraction of its size. Every Chinese open-weight lab in this table outranks every Anthropic model on it.

Read that as a trade, not a scandal. A model trained to infer what you meant will override what you said; a model trained to execute will do the literal thing and not notice that the literal thing was wrong. Which one is correct depends entirely on whether a person reads the output before it acts. Supervised, the interpreter is better. Unattended, it is the more dangerous of the two, because its failures are fluent.

Four models in this article are not scored on this axis at all: Claude Opus 5, Claude Sonnet 5, Kimi K3 and GPT-5.6 Luna. The obedience data lags the capability data by a full generation, which is the practical reason the axis gets left out of buying decisions.

Coding

SWE-bench Verified, read from the llm-stats leaderboard on 4 August 2026, 104 models.

Anthropic holds the top seven. That lead is real and it should not be argued away.

Then read the price column. Fable 5 resolves 0.950 at $10 and $50. MiniMax M3 resolves 0.805 at $0.30 and $1.20 — output roughly forty times cheaper for about fifteen points of resolution rate. DeepSeek V4 Flash Max resolves 0.790 at $0.10 and $0.20. Whether those points are worth forty times the money depends on how expensive a failed patch is in your process, which is a question about your review step and not about the model.

CNBC put the same comparison on the record from the other direction, on Kimi K3 against Anthropic's leader: "It's half the price of Claude Fable 5, the advanced model that Anthropic announced in June, even though it performs comparably across coding and knowledge work tasks."

The anomaly to act on: Claude Sonnet 5 is fifth in the world on SWE-bench at $2 and $10 — and AWS's own pricing page carries the expiry in capitals. "IMPORTANT: Claude Sonnet 5 promotional launch pricing of $2/$10 per million input/output tokens is in effect through August 31, 2026, after which the standard pricing of $3/$15 per" million takes effect. The best coding value near the top of the board becomes 50% more expensive in 27 days.

Writing

EQ-Bench creative writing Elo, read 4 August 2026: Claude Opus 5 at 2430, Kimi K3 at 2340, GPT-5.6 Sol at 2092. Meta's Muse Spark 1.1 is reported as the strongest quality per dollar in the category.

Claude leads and the margin over third is wide. Kimi K3 in second is the entry that matters commercially: second in the world at prose, open-weight, at $3 and $15 against Opus 5's $5 and $25. Where a human edits the draft anyway, that is the first substitution to test.

What changed in the last week

30 July 2026 — OpenAI cut two of three GPT-5.6 tiers. CNBC: "The company said Thursday that it's reducing the price of Terra by 20% to $2 per million input tokens and $12 per million output tokens. It's cutting the cost of Luna by 80% to 20 cents per million input tokens and $1.20 per million output tokens."

CNBC also named the cause, which is not usually stated so plainly: "Moonshot AI, a Chinese startup, released an open-weight model called Kimi K3 earlier this month that outperforms cutting-edge American offerings across some industry benchmarks, prompting a swift reaction from Silicon Valley."

The top tier was not cut. Forbes: "While the premium Sol model saw no price cut, it is now 2.5 times faster within the API." Speed instead of price at the top, price at the bottom — which is what a lab does when the pressure is on volume rather than on the frontier.

The market moved within days. On OpenRouter's weekly leaderboard read 4 August, GPT-5.6 Luna is fifth by token volume and up 738% week over week, the largest move in the top twenty; Terra is eighteenth and up 237%.

31 August 2026 — Claude Sonnet 5 reverts from $2/$10 to $3/$15.

Now — OpenRouter is running a 50% promotion on Luna and Terra. Its listed prices at this reading, $0.10/$0.60 and $1.00/$6.00, are exactly half OpenAI's post-cut rates. That is a promotion with no published end date, and a cost model built on it is built on a discount.

The list price is usually not the price

Four mechanisms move the real number, in both directions, and three of them are in the providers' own documentation.

Caching cuts input hard. Anthropic: "Instead of reprocessing the same large system prompt, document, or conversation history on every request, the API reads from cache at a fraction of the standard input price." Cache hits are 10% of base input on Anthropic; DeepSeek's cache-hit rate on V4 Flash is $0.0028 against $0.14, a fiftieth. For any workload with a stable system prompt or a repeated document, the cached input rate is the real input rate.

Batch halves everything, if you can wait. Anthropic's Batch API is 50% off input and output. AWS: "Amazon Bedrock offers select foundation models (FMs) from leading AI providers like Anthropic, Meta, Mistral AI, and Amazon for batch inference at a 50% lower price compared to on-demand inference pricing."

DeepSeek doubles during Beijing business hours. This is in DeepSeek's own pricing documentation and almost never appears in a comparison table: "During peak hours, prices will be 2x the regular prices, applicable to all billing items." Peak is 09:00–12:00 and 14:00–18:00 Beijing time. In United States Pacific time that is roughly 18:00–21:00 and 23:00–03:00 the previous day. A US team working normal hours mostly misses it; a US team running overnight batches lands in it, and pays $0.28 and $0.56 for V4 Flash rather than $0.14 and $0.28. No leaderboard's cost column knows this.

Reasoning effort is a price, not a setting. The same model at max effort emits several times the tokens of the same model at medium, so the effort level is a cost decision disguised as a quality one. OpenAI says so in its own migration guide: "When migrating from GPT-5.5 or GPT-5.4, start with your current GPT-5.5 or GPT-5.4 reasoning setting, then test the same setting and one level lower on representative tasks." And on the newest generation: "GPT-5.6 can often maintain or improve quality with fewer tokens, but the best setting depends on your workload."

The same weights, sold at four times the price

An open-weight model is not sold by one company at one price. It is served by dozens of hosts, and the spread is the largest unpriced arbitrage in this market. Read from OpenRouter's per-endpoint data on 4 August 2026, dollars per million input and output.

DeepSeek V4 Flash, twenty endpoints:

The cheapest endpoint undercuts the model's own maker by 40%.

DeepSeek V4 Pro, where the spread turns severe:

Six major hosts charge four times what DeepSeek charges for DeepSeek's own model.

GLM 5.2, where a reseller beats the maker:

Novita sells GLM 5.2 for 60% less than Z.AI sells its own model. Alibaba sells it for four times Novita's price.

Kimi K3 runs $2.90/$14.00 at Morph up to $6.00/$22.50, with most hosts including Moonshot at $3.00/$15.00. MiniMax M3 is tight: $0.24/$0.96 at GMICloud, $0.30/$1.20 nearly everywhere including MiniMax itself.

The caveat that makes the cheap column dangerous. Many discount endpoints run quantised weights — fp4 or fp8 rather than the precision the model was evaluated at. None of the benchmark scores in this article were measured on a quantised endpoint. Nobody publishes a benchmark run per endpoint. So the gap between a leaderboard score and what a $0.084 fp4 route actually delivers is unmeasured, and a 40% saving on degraded weights is not the same product at a discount.

OpenRouter states its own position: "OpenRouter passes through the pricing of the underlying providers, while pooling their uptime, so you get the same pricing you'd get from the provider directly, with a unified API and fallbacks so that you get much better uptime." The router is not the markup. The host is.

Cloudflare, answered precisely

Two Cloudflare products get confused and only one of them costs anything.

AI Gateway takes no cut of inference. Cloudflare's pricing page: "Inference pricing from providers is passed through with no markup — you pay the same per-token rates as you would directly with the provider." Core features are free. The only fee is on prepaid credit: "A 5% fee is applied to all credits purchased through Unified Billing. […] For example, a $100 credit purchase will result in a $105 charge." Log storage is capped at 100,000 logs total on the free Workers plan and ten million per gateway on paid, with Logpush at $0.05 per million requests above ten million. As a routing, caching, logging and DLP layer it is close to free and it makes tokens no cheaper.

Workers AI, where Cloudflare hosts the model, is a different product and it is not billed in tokens at all. "Workers AI is included in both the Free and Paid Workers plans and is priced at $0.011 per 1,000 Neurons." Ten thousand Neurons a day are free. Converted to per-token equivalents and set against the cheapest route for the same model string, on 4 August 2026:

Three to nine times, except where Cloudflare is the only host and the router is quoting Cloudflare's own number back. The reason to run open-weight inference on Workers AI is co-location with a Worker and the latency that buys. It is not price. Gateway and hosting are separate decisions and should be priced separately.

The hyperscalers behave differently again: Bedrock quotes Anthropic and OpenAI at their direct list prices with no wrapper premium and offers batch at half; Azure OpenAI is reported to carry a 15% to 40% overhead against the direct API. For closed frontier models the cloud marketplaces are close to neutral. For open weights they are not the cheap route.

Access, licensing and the things that stop you buying

The licence trap is new and specific. TechTimes, on the weights released 3 August: "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'". Self-hosting those weights is not licensed in those jurisdictions. The hosted API is unaffected — which means for a US buyer, "open weights" and "usable open weights" have come apart, and the API is the compliant path rather than the fallback.

The practical consequence across the table: for a United States developer the cheapest route to Chinese open-weight models is usually not the maker. It is a Western host — DigitalOcean, DeepInfra, Novita, Fireworks — or a router. That avoids a Chinese payment rail, avoids sending the request to China, and on DeepSeek V4 Flash it is also 40% cheaper than the maker. Data residency and price point the same way, which is unusual.

What is actually being used

OpenRouter's weekly leaderboard, read 4 August 2026, is the largest public window into what developers pick when every model is one key away.

Three corrections before drawing anything from it. Three of the top thirteen are free tiers, so their volume is giveaway capacity rather than demand. Token counts come from each provider's own tokenizer, so a token is a unit of telemetry rather than a standard unit of work. And a terse model burns a fraction of the tokens a reasoning model burns answering the same question, so this column structurally rewards cheap high-throughput work and understates expensive work.

On the widely quoted 58%, the provenance matters and is usually dropped. It is not OpenRouter's published figure. Benzinga: "According to data shared by The Kobeissi Letter on X on Sunday, Chinese AI models accounted for a record 58% of tokens processed by U.S. firms on the platform. The share has nearly tripled since mid-January and briefly reached 63% during the first week of July." eWeek attributes a different number to a different reader of the same platform: "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."

So: two outside parties reading one platform's data arrive at 58% and roughly 60%, neither reproducible from the public leaderboard, both describing United States-identified firms on OpenRouter only. It is a real majority of that marketplace. OpenRouter processed over 20 trillion tokens a week as of April 2026 against Google's stated roughly 19 billion tokens a minute through its model APIs and OpenAI's stated 6 billion. The marketplace is a genuine signal of developer preference and a small slice of total consumption, and everything it excludes skews American.

What to buy

Best coder, price no object. Claude Fable 5, SWE-bench Verified 0.950, $10/$50. Nothing is close on that benchmark. For agentic shell work rather than patch generation, GPT-5.6 Sol leads Terminal-Bench 2.1 at 89.5%.

Best coding value near the top. Claude Sonnet 5 at 0.852 and fifth in the world, $2/$10 until 31 August 2026, then $3/$15. Buy the reserved capacity now or re-price it in four weeks.

Best coding value outright. MiniMax M3: 0.805 on SWE-bench, first in the world on instruction following, $0.30/$1.20. It is the only model here that leads one axis, sits in the top ten of another, and costs under a dollar per million output tokens.

Cheapest model that still does real work. DeepSeek V4 Flash: 0.790 resolved, $0.14/$0.28 from the maker, $0.084/$0.168 from DigitalOcean, $0.03 per task on the Artificial Analysis suite — outside Beijing peak hours.

Best writer. Claude Opus 5 at 2430 Elo. Where a human edits anyway, Kimi K3 at 2340 costs $3/$15 against $5/$25.

Best open-weight agent for long-horizon work. GLM-5.2, which Artificial Analysis calls "the clear leader among open-weight models" on AA-Briefcase, offering "an attractive agentic capability vs. cost tradeoff" — and which is 60% cheaper from Novita than from Z.AI.

Most obedient. MiniMax M3 at 82.9%, then Nemotron 3 Ultra at 81.4% and Grok 4.3 at 81.3%. If output must conform to a schema or a rule set with no human in the loop, start here rather than at the top of the intelligence index.

Most underpriced right now. GPT-5.6 Luna at $0.20/$1.20 after an 80% cut, index 51, cost per task $0.03–0.05 — currently sold at half that again on OpenRouter. Its obedience is unmeasured, which is the reason to test it rather than to adopt it blind.

Where not to buy. Open-weight text on Workers AI at three to nine times the cheapest route. DeepSeek V4 Pro from any of six hosts at $1.74 when the maker charges $0.435. GLM 5.2 from Alibaba at $2.31 when Novita charges $0.566. And any Chinese endpoint during Beijing peak hours without checking whether the doubling applies.

What would change these answers

The clocks. Claude Sonnet 5 reverts 31 August 2026. OpenRouter's Luna and Terra promotion has no published end date. DeepSeek's peak-hour doubling applies daily.

The missing obedience scores. Claude Opus 5, Claude Sonnet 5, Kimi K3 and GPT-5.6 Luna are unscored on AA-IFBench. Four scored Anthropic models cluster between 41.2% and 63.5%, which is a family pattern rather than a prediction about the two newest.

Quantisation. No benchmark score here was measured on the discounted quantised endpoints that make the price tables attractive, and no one publishes a benchmark run per endpoint. This is the largest unmeasured variable on the page.

Licensing. MiniMax H3's territory exclusion is six days old. If that clause becomes a pattern across Chinese labs, "open weight" stops meaning "deployable" for Western buyers and the routing question changes shape entirely.

And the number no public benchmark reports: cost per accepted task on real work, with retries, tool calls, verification and escalation charged to the model that needed them. Every figure above is a list price or a benchmark score. A model that is cheap per token and needs three attempts is not cheap, and the only way to know is to run one frozen task set through every candidate and count what passed.

Index log

• 4 August 2026. First edition. First-party prices read from OpenAI's model documentation and migration guide, Cloudflare's AI Gateway and Workers AI pricing pages, Anthropic's pricing documentation, AWS Bedrock pricing, DeepSeek's pricing documentation, Moonshot's and Z.AI's pricing pages, and OpenRouter's public model and per-endpoint APIs. Capability read from the Artificial Analysis leaderboard and AA-Briefcase write-up, the llm-stats SWE-bench Verified leaderboard, the BenchLM mirror of AA-IFBench, and the EQ-Bench creative writing leaderboard. The 30 July price cut is reported by CNBC and Forbes. The July 2026 OpenRouter share figures are third-party readings by The Kobeissi Letter and Bloomberg, not OpenRouter publications, and are labelled as such above.

MODEL INDEX — 2026-08-04 (America/Los_Angeles)

Best coder             Claude Fable 5      SWE-bench Verified 0.950      $10.00 / $50.00
Best agentic coder     GPT-5.6 Sol xhigh   Terminal-Bench 2.1 89.5%      $5.00 / $30.00
Best writer            Claude Opus 5       EQ-Bench Elo 2430             $5.00 / $25.00
Most obedient          MiniMax M3          AA-IFBench 82.9%, rank 1/144  $0.30 / $1.20
Best open agent        GLM-5.2             AA-Briefcase open leader      $1.40 / $4.40
Best value overall     MiniMax M3          SWE 0.805 + IFBench 1st       $0.30 / $1.20
Cheapest capable       DeepSeek V4 Flash   SWE 0.790                     $0.14 / $0.28
Most used              DeepSeek V4 Flash   6.92T tokens/week             OpenRouter
Most underpriced       GPT-5.6 Luna        -80% on 2026-07-30            $0.20 / $1.20
Expiring 2026-08-31    Claude Sonnet 5     intro rate ends               $2/$10 -> $3/$15

Cheapest venue, DeepSeek V4 Flash   DigitalOcean $0.084 / $0.168   maker charges $0.140 / $0.280
Cheapest venue, DeepSeek V4 Pro     DeepSeek     $0.435 / $0.870   six hosts charge 4.0x
Cheapest venue, GLM 5.2             Novita       $0.566 / $1.778   maker Z.AI charges 2.5x
AI Gateway markup on tokens         none         5% on prepaid credits only
Workers AI vs cheapest route        3x to 9x     open-weight text
DeepSeek peak-hour multiplier       2.0x         09:00-12:00 and 14:00-18:00 Beijing
Batch discount, Anthropic + Bedrock 50%          off input and output
Cache-hit input, Anthropic          10%          of base input
Cache-hit input, DeepSeek V4 Flash  $0.0028      against $0.140 base

The next edition should answer the question this one cannot: what each of these costs per task that actually passed, on the same frozen work, with its failures charged to it — because the model that resolves the most GitHub issues is 66th at doing what it is told, and no published number tells a buyer what that trade is worth.