Live data for 2026-10 (recalculated daily)
· Methodology v1
· Methodology
· History
· JSON
#
Tier
Model
fedi-index
Percentile
Value
Sources
Score decomposition
9 A Kimi K3provisional Moonshot AI · Open weights 1,468 ±19 94 +44 A ›
19 A GLM 5.2provisional Zhipu AI · Open weights 1,444 ±17 86 +45 A ›
21 A DeepSeek V4 Pro 0423provisional DeepSeek · Open weights 1,441 ±16 84 +33 A ›
24 A Kimi K2.6provisional Moonshot AI · Open weights 1,436 ±19 82 +44 A ›
25 A GLM 5.3provisional Zhipu AI · Open weights 1,435 ±23 81 +60 A ›
26 A GLM 5.1provisional Zhipu AI · Open weights 1,432 ±16 80 +36 A ›
27 B Qwen3.5 397B A17Bprovisional Alibaba · Open weights 1,429 ±14 80 +27 A ›
30 B Inklingprovisional Thinking Machines Lab · Open weights 1,426 ±19 77 +27 A ›
31 B Kimi K2.5provisional Moonshot AI · Open weights 1,424 ±15 76 +36 A ›
32 B GLM 5.3 Flashprovisional Zhipu AI · Open weights 1,423 ±21 76 +105◆ A ›
37 B MiMo-V2.5-Proprovisional Xiaomi · Open weights 1,421 ±15 72 +80 A ›
38 B GLM 4.5provisional Zhipu AI · Open weights 1,420 ±16 71 +18 A ›
48 B DeepSeek V4 Flash 0423provisional DeepSeek · Open weights 1,411 ±17 63 +71 A ›
49 B Inkling Smallprovisional Thinking Machines Lab · Open weights 1,410 ±21 62 +38 A ›
57 B Qwen3 Next 80B A3B Instructprovisional Alibaba · Open weights 1,404 ±23 56 +32 A ›
59 B GLM 4.6provisional Zhipu AI · Open weights 1,402 ±21 54 +26 A ›
61 B Qwen3.8 27Bprovisional Alibaba · Open weights 1,402 ±23 53 +46 A ›
66 C MiniMax M3provisional MiniMax · Open weights 1,395 ±15 49 +50 A ›
72 C DeepSeek V3.1provisional DeepSeek · Open weights 1,388 ±21 44 +19 A ›
76 C Step 3.5 Flashprovisional StepFun · Open weights 1,376 ±17 41 +50 A ›
77 C GLM 4.5 Airprovisional Zhipu AI · Open weights 1,374 ±15 40 -2 A ›
83 C Qwen3 30B A3B Instruct 2507provisional Alibaba · Open weights 1,367 ±15 35 +25 A ›
86 C gpt-oss-120bprovisional OpenAI · Open weights 1,362 ±18 33 -2 A ›
87 C MiMo-V2-Flashprovisional Xiaomi · Open weights 1,355 ±20 32 +55◆ A ›
90 C Qwen3 Coder 480B A35Bprovisional Alibaba · Open weights 1,347 ±14 30 -22 A ›
92 C MiMo-V2.5provisional Xiaomi · Open weights 1,342 ±19 28 +75 A ›
94 C Llama 3.3 Nemotron Super 49B v1.5provisional NVIDIA · Open weights 1,339 ±20 27 -40 A ›
97 D Mistral Small 3.2provisional Mistral AI · Open weights 1,323 ±18 24 -14 A ›
98 D gemma-3n-e4b-itprovisional Google · Open weights 1,322 ±16 24 — A ›
99 D MiniMax M2.7provisional MiniMax · Open weights 1,314 ±15 23 +32 A ›
100 D Qwen3 30B A3Bprovisional Alibaba · Open weights 1,309 ±15 22 -29 A ›
104 D MiniMax M1provisional MiniMax · Open weights 1,285 ±15 19 -36 A ›
108 D MiniMax M2.5provisional MiniMax · Open weights 1,248 ±25 16 +9 A ›
119 D mixtral-8x22b-instruct-v0.1provisional Mistral AI · Open weights 1,159 ±15 7 -236 A ›
…
A Arena (human votes) ◆ Pareto frontier: nothing is both better and cheaper
fedi-index is an Elo-equivalent on a fixed scale: comparable between months within one methodology version. Tiers S–D: by percentile in the slice, and no better than the confidence interval allows (cut-offs in the methodology). This slice has one source family, so all its scores are provisional.
Methodology
fedi-index © fedi.software, CC BY 4.0; components keep the licences of their sources. · Sources:
LMArena (CC BY 4.0),
Epoch AI (CC BY 4.0),
τ²-bench (Sierra) (MIT),
models.dev (MIT),
LiteLLM (MIT)
Arena Leaderboard Dataset by LMArena, CC BY 4.0. Modified by fedi.software (equated, weighted, aggregated). Not endorsed by LMArena.
Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. CC BY. ECI by Epoch AI; Epoch-run results only. Modified by fedi.software.
τ²-bench leaderboard, © Sierra Research, MIT License (Yao et al. 2024, arXiv:2406.12045; Barres et al. 2025, arXiv:2506.07982). Sierra-run text submissions only.
Pricing: models.dev, MIT License, © 2025 models.dev.
Pricing: LiteLLM model_prices_and_context_window.json, MIT License, © 2023 Berri AI.