Ranked by a published coding benchmark, with the benchmark named in every row because the scores are not comparable across it. Terminal-Bench 2.1 and Terminal-Bench 4.0 are different tests, and a model scoring 88.2 on one is not beating a model scoring 57.7 on the other. That caveat is the most useful thing on this page.
Every model in our index with a published coding benchmark, ordered by score within its own benchmark. Numbers come from the vendor or the model card. We have deliberately not normalised across benchmarks or built a composite score, because doing so would invent a comparison the underlying data does not support. Where we have run our own evals, that is on customer tasks under NDA and is not what this table shows.
| Model | Vendor | Weights | Benchmark | Context | $ / 1M in · out |
|---|---|---|---|---|---|
01 GLM-5.3 zai-org/GLM-5.3 Level with GPT-5.6 Sol on Terminal-Bench 2.1 at a seventh of the input price, and state of the art on CyberGym vulnerability discovery. | Z.ai | Open | 88.2Terminal-Bench 2.1 | 1M | $1.40 · $4.40$0.26 cached |
02 GLM-5.3-Flash zai-org/GLM-5.3-Flash MIT at 320B, multimodal, and four points behind its own flagship. The most permissive licence attached to a model this size. | Z.ai | Open | 84.3Terminal-Bench 2.1 | 300K | Self-hosted |
03 Qwen3.8-Flash-Next Qwen/Qwen3.8-Flash-Next Beats Claude Opus by 22 points on AndroidWorld device automation. Frontier scores at roughly 6B inference cost, on a 180B machine. | Alibaba | Open | 62.5SWE-bench Pro | 262K, 1M max | Self-hosted |
04 Qwen3.8-27B Qwen/Qwen3.8-27B Beats Claude Opus 4.6 Max on SWE-bench Pro and OSWorld in Alibaba's table, at 27B under Apache 2.0. Second most-liked model on Hugging Face. | Alibaba | Open | 61.7SWE-bench Pro | 262K, 1M max | Self-hosted |
05 Claude Mythos 5.1 Fable 5.1's specs and price, restricted to Project Glasswing participants. Scores 60.9 on Terminal-Bench 4.0 against Fable's 55.8. | Anthropic | Closed | 60.9%Terminal-Bench 4.0 | 1M | $10 · $50$0.25 cached |
06 GPT-6 Astra First model rated Critical for cyber under the Preparedness Framework. Records on computer use and terminals, fourth on a third-party intelligence index. | OpenAI | Closed | 57.7%Terminal-Bench 4.0 | 1.05M | $10 · $50$1 cached |
07 Claude Fable 5.1 Tops the Artificial Analysis Intelligence Index at roughly 66. Cache reads at $0.25, a quarter of Fable 5 and of every other Claude model. | Anthropic | Closed | 55.8%Terminal-Bench 4.0 | 1M | $10 · $50$0.25 cached |
08 Claude Opus 5 Anthropic's recommended starting tier and the coding arena leader. 63.1 on the Artificial Analysis index at half Fable pricing. | Anthropic | Closed | 52.3%Terminal-Bench 4.0 | 1M | $5 · $25 |
09 GPT-5.6 Sol The prior OpenAI flagship and still the right default for most production work at two and a half times less than Astra. | OpenAI | Closed | 37.3%Terminal-Bench 4.0 | 1.05M | $4 · $20$0.40 cached |
Every row carries the date we last verified it. Prices are list rates at the standard tier and exclude batch discounts, long-context surcharges and regional variation. Hugging Face download and like counts are pulled from the API rather than retyped, and they measure adoption rather than quality.
GLM-5.3 at 88.2 and GPT-6 Astra at 57.7 are on Terminal-Bench 2.1 and Terminal-Bench 4.0 respectively. The 4.0 suite is substantially harder. Sorting a table like this by raw score produces a ranking that looks authoritative and means very little, which is exactly why we name the harness in the row rather than in a footnote.
GLM-5.3 runs level with GPT-5.6 Sol on Terminal-Bench 2.1 at $1.40 against $4.00 on input. Qwen3.8-Flash-Next posts 62.5 on SWE-bench Pro, ahead of DeepSeek-V4 at 56.0. For mainstream coding work the frontier premium buys less than it did six months ago, and it still buys real headroom on the hardest tasks.
GLM-5.3 is level with Sol on Terminal-Bench 2.1 and five points behind Fable 5 on the harder 3.0. That shape, competitive on ordinary work and falling off at the top end, is what a routing rule should encode. A single headline benchmark hides exactly the information you need.
Z.ai's flat-rate Coding Plan at $18 to $168 a month is unusual and underrated. One runaway agent loop can cost more than a month of normal use, and teams are consistently more damaged by an unpredictable bill than a high one. Very few vendors offer this shape, and it unblocks adoption where a per-token discount does not.
Every model we integrate runs through the same operating system. Three pillars, sixteen layers, one Compound Growth Loop. The methodology that keeps AI work from rotting after the first ship.
Read the K-FrameworkDirect API integration with the model. No LangChain, no orchestration vendor, no agent framework built on quicksand. Typed contracts, the same way we wire up Postgres.
An eval suite built from your real tasks gates every prompt and model change. Quality is measured before it ships, not vibed in a demo.
Governance, audit, and oversight wired in from day one. Who called what, with which prompt version, at what cost. Your auditors get answers, not screenshots.
A model in production without observability is roulette. We instrument every integration so engineering and finance can see the same numbers, and so a regression at 3am surfaces before a customer opens a ticket.
Tokens in, tokens out, dollars spent. Sliced by feature, tenant, and route. Budgets enforced where it matters.
Real distributions, not averages. We know which routes are slow, and why.
The same eval suite that gates a release runs continuously in production. A regression on real traffic surfaces fast.
PII scrubbed at the proxy, shipped to your SIEM. Retention controls match your compliance window.
Dashboards your team owns, not ours. At handoff you get the queries, the alerts, and the runbook. We are not in the path to read your metrics.
