Capability ranking
Use this coding AI model leaderboard to compare the current ranking from the LLMBoard Coding Score, aggregated across eligible coding and tool-use benchmarks.
Updated 2026-10-22
This leaderboard ranking orders models by their LLMBoard Coding Score aggregated from eligible benchmark evidence.
Rank | Model | LLMBoard Coding Score | Official input / 1M | Official output / 1M | Output speed | Context |
|---|
| Rank01 | ModelAN | LLMBoard Coding Score97.08 | Official input / 1MN/A | Official output / 1MN/A | Output speedN/A | ContextN/A |
| Rank02 | ModelAN | LLMBoard Coding Score96.24 | Official input / 1M$4 | Official output / 1M$20 | Output speed12.33 tok/s | Context1M |
| Rank03 | ModelME | LLMBoard Coding Score92.38 | Official input / 1M$1.25 | Official output / 1M$4.25 | Output speedN/A | Context1M |
| Rank04 | ModelAN | LLMBoard Coding Score89.03 | Official input / 1M$10 | Official output / 1M$50 | Output speedN/A | Context1M |
| Rank05 | ModelAN | LLMBoard Coding Score88.90 | Official input / 1M$2 | Official output / 1M$10 | Output speed42.00 tok/s | Context1M |
| Rank06 | ModelGO | LLMBoard Coding Score86.46 | Official input / 1M$0.75 | Official output / 1M$3.75 | Output speed55.66 tok/s | Context1M |
| Rank07 | ModelME | LLMBoard Coding Score85.81 | Official input / 1MN/A | Official output / 1MN/A | Output speedN/A | ContextN/A |
| Rank08 | ModelOP | LLMBoard Coding Score85.15 | Official input / 1M$10 | Official output / 1M$50 | Output speedN/A | Context1.1M |
| Rank09 | ModelOP | LLMBoard Coding Score81.50 | Official input / 1M$4 | Official output / 1M$20 | Output speed25.46 tok/s | Context1.1M |
| Rank10 | ModelFA | LLMBoard Coding Score79.21 | Official input / 1M$3 | Official output / 1M$15 | Output speedN/A | Context1M |
| Rank11 | ModelAN | LLMBoard Coding Score78.09 | Official input / 1M$5 | Official output / 1M$25 | Output speed42.00 tok/s | Context1M |
| Rank12 | ModelAN | LLMBoard Coding Score77.14 | Official input / 1M$0.10 | Official output / 1M$0.50 | Output speedN/A | Context1M |
| Rank13 | ModelAN | LLMBoard Coding Score76.14 | Official input / 1M$5 | Official output / 1M$25 | Output speedN/A | Context1M |
| Rank14 | ModelDE | LLMBoard Coding Score75.82 | Official input / 1M$0.66 | Official output / 1M$1.98 | Output speedN/A | Context1M |
| Rank15 | ModelMA | LLMBoard Coding Score75.71 | Official input / 1M$3 | Official output / 1M$15 | Output speedN/A | Context1M |
| Rank16 | ModelAC | LLMBoard Coding Score75.31 | Official input / 1M$2.5 | Official output / 1M$7.5 | Output speedN/A | Context1M |
| Rank17 | ModelME | LLMBoard Coding Score71.59 | Official input / 1M$4 | Official output / 1M$20 | Output speedN/A | Context1M |
| Rank18 | ModelRA | LLMBoard Coding Score71.34 | Official input / 1MN/A | Official output / 1MN/A | Output speedN/A | Context1M |
| Rank19 | ModelAN | LLMBoard Coding Score71.22 | Official input / 1M$2 | Official output / 1M$10 | Output speed42.00 tok/s | Context1M |
| Rank20 | ModelDE | LLMBoard Coding Score70.93 | Official input / 1M$0.15 | Official output / 1M$0.60 | Output speedN/A | Context1M |
| Rank21 | ModelMI | LLMBoard Coding Score70.83 | Official input / 1M$0.15 | Official output / 1M$0.60 | Output speedN/A | Context1M |
| Rank22 | ModelAN | LLMBoard Coding Score70.44 | Official input / 1M$5 | Official output / 1M$25 | Output speed42.00 tok/s | Context1M |
| Rank23 | ModelAC | LLMBoard Coding Score68.41 | Official input / 1M$0.15 | Official output / 1M$0.47 | Output speedN/A | Context1M |
| Rank24 | ModelOP | LLMBoard Coding Score68.34 | Official input / 1MN/A | Official output / 1MN/A | Output speedN/A | ContextN/A |
| Rank25 | ModelAC | LLMBoard Coding Score68.09 | Official input / 1MN/A | Official output / 1MN/A | Output speedN/A | ContextN/A |
| Rank26 | ModelAC | LLMBoard Coding Score67.80 | Official input / 1MN/A | Official output / 1MN/A | Output speed44.00 tok/s | Context128K |
| Rank27 | ModelOP | LLMBoard Coding Score67.74 | Official input / 1M$2 | Official output / 1M$12 | Output speed43.35 tok/s | Context1.1M |
| Rank28 | ModelGO | LLMBoard Coding Score67.72 | Official input / 1M$0.75 | Official output / 1M$3.75 | Output speed409.96 tok/s | Context1M |
| Rank29 | ModelMA | LLMBoard Coding Score66.90 | Official input / 1M$0.95 | Official output / 1M$4 | Output speedN/A | Context262.1K |
| Rank30 | ModelAN | LLMBoard Coding Score66.86 | Official input / 1M$5 | Official output / 1M$25 | Output speed42.00 tok/s | Context1M |
Official PAYG prices appear here. Third-party offers remain on the pricing and model detail pages.
Key findings from the current coding leaderboard ranking and its supporting benchmark coverage.
Claude Mythos leads this page at 97.08, 0.84 points ahead of Claude Opus 5.5.
DeepSeek-V4-Pro is the highest-ranked open-weight option at #14. Claude Haiku 5.5 has the lowest official input price among ranked models.
The leading coding scores in this benchmark-backed ranking, with the axis focused on the competitive leaderboard range.
Compare coding benchmark strength with each model's broader capability score and leaderboard position.
Official vendor API prices plotted against the coding metric used on this page. Price remains a separate decision signal.
Vendor concentration and model access among the first 25 products in the ranking.
A data-backed look at the first five models in this coding leaderboard ranking, including benchmark context, price and output speed where available.
Common questions about the Best AI for Coding leaderboard, benchmark evidence and ranking method.
Claude Mythos is currently ranked first with a coding score of 97.08.
The current leaders are Claude Mythos (97.08 LLMBoard Coding Score), Claude Opus 5.5 (96.24 LLMBoard Coding Score), and Muse Spark 1.3 (92.38 LLMBoard Coding Score).
Claude Haiku 5.5 has the lowest current official input price among ranked models at $0.1 per 1M tokens.
The fastest matched records are Llama 4 Scout (776.10 tok/s via Groq), Llama 4 Maverick (638.70 tok/s via Sambanova), and Gemini 3.7 Flash (409.96 tok/s via Google).
DeepSeek-V4-Pro is the highest-ranked open-weight model at rank #14.
Llama 4 Scout has the largest listed context window at 10M tokens.
Each model appears once using its current scored version. The leaderboard ranking follows the capability named in the title, while the overall page uses the LLMBoard score aggregated from eligible benchmark evidence.
This page currently ranks 132 unique model products.
Main price columns use the model vendor's official standard PAYG API rate. Eligible third-party offers appear only in separately labeled columns, and unavailable official prices display as N/A.
No. Arena results are displayed as an independent signal and are not included in the current LLMBoard capability score.
Ranking basisThis coding AI model leaderboard uses the coding capability score shown on this page. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Claude Mythos is currently the best-ranked LLM for coding with a 97.08 coding score. The score is a relative ranking signal, so price, speed, context and evidence coverage should still be checked separately.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.