Best models for coding agents
An agent works for minutes at a time without anyone watching, so the wait before the first token is close to irrelevant. What decides it is whether the model writes code that passes, drives tools without losing the thread, and holds a large repository in context — and what that costs over a long session, since an agent burns tokens at a rate a chat never does.
As of 11 Oct 2026, Qwen3.8-Flash-Next fits this best, with coding of 67 and tool use of 65.
How this is weighted
- 35%Codingthe work is writing code that actually runs
- 30%Tool usean agent spends most of its turns calling tools and reading what came back, not composing prose
- 20%Long contexta real repository does not fit in a small window, and attention that degrades over length shows up as forgotten decisions
- 15%Price per 1Msessions run long, so a price difference that is invisible in a chat is the whole budget here
202 of 434 ranked models qualify: the rest either miss one of the measurements the weighting depends on, or fall outside a limit above. We would rather leave a model out than score it on the dimensions it happens to have.
| # | Model | Fit | Coding | Tool use | Long context | Price per 1M | Context window |
|---|---|---|---|---|---|---|---|
| 1 |  Qwen3.8-Flash-NextAlibaba | 85 | 67 | 65 | 65 | $0.230 | 256k |
| 2 |  Claude Opus 5.5maxAnthropic | 85 | 83 | 69 | 68 | $8.00 | 1M |
| 3 |  Muse Spark 1.3maxMeta | 85 | 65 | 70 | 67 | $2.00 | 1.0M |
| 4 | Claude Sonnet 5.5maxAnthropic | 84 | 70 | 70 | 67 | $4.00 | 1M |
| 5 |  Gemini 3.7 FlashmediumGoogle | 84 | 68 | 64 | 67 | $1.50 | 1.0M |
| 6 | Claude Fable 5.1maxAnthropic | 84 | 75 | 70 | 68 | $20.00 | 1M |
| 7 | Muse SparkMeta | 83 | 65 | 66 | 64 | – | 262k |
| 8 |  GLM 5.3 FlashZhipu AI | 83 | 63 | 63 | 65 | $0.238 | 1M |
| 9 |  GPT-5.6 SolhighOpenAI | 82 | 70 | 73 | 66 | $8.00 | 1.1M |
| 10 | GPT-6 AstraOpenAI | 82 | 83 | 75 | – | $20.00 | 1.1M |
| 11 | GPT-6.1 SolmaxOpenAI | 81 | 72 | 64 | 67 | $4.00 | 1.1M |
| 12 |  Kimi K3maxMoonshot AI | 81 | 65 | 66 | 70 | $6.00 | 1.0M |
| 13 | Gemini 4 ArgonhighGoogle | 81 | 72 | 68 | 65 | $4.00 | 1M |
| 14 | Gemini 3.8 FlashmediumGoogle | 80 | 58 | 75 | 67 | $1.50 | 1.0M |
| 15 |  DeepSeek V4.1 FlashmaxDeepSeek | 80 | 64 | 56 | 67 | $0.525 | 1M |
| 16 |  Grok 4.6mediumSpaceXAI | 79 | 61 | 74 | 66 | $3.00 | 500k |
| 17 | Claude Opus 5Anthropic | 79 | 68 | 71 | – | $10.00 | 1M |
| 18 | GPT-5.3-CodexxhighOpenAI | 79 | 61 | 80 | 67 | $4.81 | 400k |
| 19 |  MiMo-V2.6-ProXiaomi | 79 | 62 | 57 | 69 | $0.548 | 1.0M |
| 20 | GPT-6 SolmaxOpenAI | 78 | 68 | 60 | 67 | $4.00 | 1.1M |
| 21 | Mimo v2 OmniXiaomi | 78 | 63 | 64 | 63 | – | 256k |
| 22 | Claude Fable 5maxAnthropic | 78 | 74 | 65 | 66 | $20.00 | 1M |
| 23 | GPT-5.5highOpenAI | 77 | 61 | 70 | 68 | $11.25 | 1.1M |
| 24 | GPT-5.4xhighOpenAI | 76 | 65 | 64 | 66 | $5.63 | 1.1M |
| 25 | GPT-5.6 TerramaxOpenAI | 75 | 65 | 59 | 67 | $4.50 | 1.1M |
| 26 | Qwen3.8 MaxmaxAlibaba | 74 | 65 | 58 | 65 | $3.00 | 1M |
| 27 | Qwen3.6 MaxmaxAlibaba | 74 | 57 | 72 | 66 | $2.92 | 262k |
| 28 | o3OpenAI | 73 | 62 | 70 | 63 | $3.50 | 200k |
| 29 | GLM 5.2maxZhipu AI | 73 | 62 | 63 | 64 | $2.15 | 1M |
| 30 | GLM 5.3maxZhipu AI | 73 | 62 | 61 | 65 | $2.15 | 1M |
| 31 | Muse Spark 1.1xhighMeta | 73 | 64 | 59 | 64 | $2.00 | 1.0M |
| 32 |  Step 5 PreviewStepFun | 72 | 66 | 47 | 70 | $1.43 | 1M |
| 33 | Claude Opus 4.7Anthropic | 72 | 62 | 68 | – | $10.00 | 1M |
| 34 | DeepSeek V4 PromaxDeepSeek | 72 | 58 | 64 | 65 | $1.98 | 1M |
| 35 | Claude Opus 4.5thinkingAnthropic | 71 | 61 | 75 | 64 | $10.00 | 200k |
| 36 | Grok 4SpaceXAI | 71 | 61 | 58 | 68 | $6.00 | 256k |
| 37 | Claude Opus 4.6Anthropic | 71 | 60 | 69 | – | $10.00 | 1M |
| 38 | MiMo-V2.6-FlashXiaomi | 71 | 60 | 56 | 62 | $0.175 | 1.0M |
| 39 | Qwen3.8 27BxhighAlibaba | 70 | 53 | 67 | 66 | $1.13 | 262k |
| 40 | GPT-5.2xhighOpenAI | 70 | 63 | 55 | 67 | $4.81 | 128k |
How to read this
Fit is a percentile blend, not a score out of a hundred: each dimension is ranked against every other model that could be judged here, then combined with the weights above. It says how well a model matches this job compared with the alternatives — a model can fit voice work superbly and sit well down the quality leaderboard, which is the point of ranking by job rather than by index.
Disagree with the weighting? That is a reasonable thing to do, which is why it is printed rather than hidden. To set your own constraints instead, use the model finder.
Cite as: BenchLeader, “Best models for coding agents”, https://www.benchleader.com/use/coding-agents, data as of 11 Oct 2026.