Coding · benchmark vs real usage
Best AI model for coding: benchmarks vs. what developers use
On coding benchmarks, Anthropic: Claude Fable 5.1 (batch) leads with a coding index of 80.7. But in real production traffic across file I/O, debugging, code review, and code generation tasks, DeepSeek: DeepSeek V4.1 Flash (batch) handles the largest share at 53.7%- measured directly from OpenRouter's task-classified traffic, not a benchmark opinion.
- #1 DeepSeek: DeepSeek V4.1 Flash (batch) - 53.7% of coding traffic
- #2 Z.ai: GLM 5.3 Flash (batch) - 12.8% of coding traffic · coding index 71.5
- #3 OpenAI: GPT-6 Luna (batch) - 6.0% of coding traffic
- #4 OpenAI: GPT-6.1 Sol (batch) - 5.0% of coding traffic
- #5 Crouton Alpha - 4.8% of coding traffic
- #6 Tencent: Hy4 preview - 2.6% of coding traffic
- #7 Xiaomi: MiMo-V2.6-Flash - 2.6% of coding traffic
- #8 Z.ai: GLM 5.3 (batch) - 1.8% of coding traffic · coding index 74.8
- #9 MoonshotAI: Kimi K3.1 Pro - 1.7% of coding traffic
- #10 MiniMax: MiniMax M3.1 Flash - 1.5% of coding traffic
- #11 OpenAI: GPT-5.6 Luna (batch) - 1.5% of coding traffic · coding index 71.4
- #12 Meta: Muse Spark 1.3 Contributor - 1.3% of coding traffic
- #13 Tencent: Hy4 - 1.1% of coding traffic
- #14 DeepSeek: DeepSeek V4 Flash 0423 - 0.9% of coding traffic · coding index 69.1
- #15 Tencent: Hy3 - 0.9% of coding traffic
| # | Model | Coding traffic share | Coding index |
|---|---|---|---|
| 01 | DeepSeek: DeepSeek V4.1 Flash (batch) | 53.7% | - |
| 02 | Z.ai: GLM 5.3 Flash (batch) | 12.8% | 71.5 |
| 03 | OpenAI: GPT-6 Luna (batch) | 6.0% | - |
| 04 | OpenAI: GPT-6.1 Sol (batch) | 5.0% | - |
| 05 | Crouton Alpha | 4.8% | - |
| 06 | Tencent: Hy4 preview | 2.6% | - |
| 07 | Xiaomi: MiMo-V2.6-Flash | 2.6% | - |
| 08 | Z.ai: GLM 5.3 (batch) | 1.8% | 74.8 |
| 09 | MoonshotAI: Kimi K3.1 Pro | 1.7% | - |
| 10 | MiniMax: MiniMax M3.1 Flash | 1.5% | - |
| 11 | OpenAI: GPT-5.6 Luna (batch) | 1.5% | 71.4 |
| 12 | Meta: Muse Spark 1.3 Contributor | 1.3% | - |
| 13 | Tencent: Hy4 | 1.1% | - |
| 14 | DeepSeek: DeepSeek V4 Flash 0423 | 0.9% | 69.1 |
| 15 | Tencent: Hy3 | 0.9% | - |
| 16 | NVIDIA: Nemotron 3.5 Super VL (free) | 0.6% | - |
| 17 | Anthropic: Claude Opus 5.5 (batch) | 0.5% | - |
| 18 | NVIDIA: Nemotron 3.5 Lightning (free) | 0.2% | 26.8 |
| 19 | Z.ai: GLM 5.2 | 0.2% | 68.8 |
| 20 | Anthropic: Claude Haiku 5.5 (batch) | 0.1% | - |
| 21 | NVIDIA: Nemotron 3 Ultra (free) | 0.0% | 49.3 |
| 22 | Thinking Machines: Inkling | 0.0% | 52.1 |
| 23 | Anthropic: Claude Sonnet 5.5 (batch) | 0.0% | - |
Coding traffic share is a weighted rollup of OpenRouter's real classified traffic across nine coding-task categories (file I/O, repo scanning, frontend/UI, DevOps config, shell execution, SQL/database, debugging, code review, code generation), weighted by each category's own share of total classified traffic.
Frequently asked questions
What is the best AI model for coding?
On benchmarks, Anthropic: Claude Fable 5.1 (batch) leads coding tasks with a coding index of 80.7. In real usage, DeepSeek: DeepSeek V4.1 Flash (batch) handles the largest share of actual coding traffic (54%).
What model do developers actually use for coding?
DeepSeek: DeepSeek V4.1 Flash (batch) handles 53.7% of real coding-task traffic on OpenRouter, measured across file I/O, debugging, code review, and code generation tasks.