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Best AI model for coding

For coding, Claude Fable 5.1 scores highest on the current data (69.4), weighting coding 60%, agentic & tool use 25%, instruction following 15%. The cheapest model in the top 10 is Claude Sonnet 5.5 at $2 / $10 per million tokens.

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Weights: Coding 60%, Agentic & Tool Use 25%, Instruction Following 15%. Coding work is mostly repository-level bug fixing and multi-step edits, so coding and agentic results carry the most weight.

Best AI model for coding
#ModelProviderFitCodingAgentic & Tool UseInstruction FollowingInput $/MOutput $/M
1Claude Fable 5.1Anthropic69.474.750.779.2$10$50
2GPT-6 AstraOpenAI68.973.752.976.3$10$50
3Claude Fable 5Anthropic67.770.654.078.6$10$50
4Claude Opus 5.5Anthropic66.571.945.380.0$4$20
5Claude Opus 5Anthropic66.367.555.679.2$5$25
6Claude Sonnet 5.5Anthropic63.367.345.078.3$2$10
7GPT-5.6 SolOpenAI63.365.150.377.7$4$20
8Gemini 4 ArgonGoogle61.562.049.280.1——
9Claude Opus 4.7Anthropic59.559.647.978.4$5$25
10Claude Opus 4.8Anthropic59.559.947.677.4$5$25
11GPT-6.1 SolOpenAI59.463.239.677.0$2$10
12GPT-5.5OpenAI59.258.250.777.5$5$30
13Claude Opus 4.6Anthropic59.057.251.179.5$5$25
14Kimi K3 (open weights)Moonshot AI58.761.041.877.7$3$15
15Gemini 3.8 FlashGoogle57.759.241.878.0$0.75$3.75
16GPT-6 SolOpenAI56.660.137.274.5$2$10
17GLM-5.3 (open weights)Z.ai (Zhipu)56.459.536.477.5$1.40$4.40
18Claude Opus 4.5Anthropic56.354.847.377.5$5$25
19Grok 4.6xAI56.258.539.475.4$2$6
20GPT-5.6 TerraOpenAI56.157.740.176.4$2$12
21Gemini 3.7 FlashGoogle55.956.242.177.7$0.75$3.75
22Claude Sonnet 5Anthropic55.455.542.876.3$2$10
23Muse Spark 1.3Meta55.356.638.677.5$1.25$4.25
24Qwen3.8 MaxAlibaba (Qwen)55.153.545.477.6$2$6
25Grok 4.7xAI55.158.036.774.1$2$6

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Top two head to head: Claude Fable 5.1 vs GPT-6 Astra

Frequently asked questions

What is the best ai model for coding?

For coding, Claude Fable 5.1 scores highest on the current data (69.4), weighting coding 60%, agentic & tool use 25%, instruction following 15%. The cheapest model in the top 10 is Claude Sonnet 5.5 at $2 / $10 per million tokens.

How is this shortlist built?

Coding work is mostly repository-level bug fixing and multi-step edits, so coding and agentic results carry the most weight. Each model's category scores are blended with those weights; only ranked models with results in every needed category are listed.

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