Live data for 2026-10 (recalculated daily)
· Methodology v1
· Methodology
· History
· JSON
#
Tier
Model
fedi-index
Percentile
Value
Sources
Score decomposition
14 A Kimi K3provisional Moonshot AI · Open weights 1,445 ±20 91 +44 A ›
22 A GLM 5.2provisional Zhipu AI · Open weights 1,438 ±17 85 +45 A ›
23 A GLM 5.1provisional Zhipu AI · Open weights 1,437 ±16 85 +36 A ›
28 A MiMo-V2.5-Proprovisional Xiaomi · Open weights 1,432 ±15 81 +80 A ›
30 B DeepSeek V4 Pro 0423provisional DeepSeek · Open weights 1,432 ±15 80 +33 A ›
33 B GLM 5.3 Flashprovisional Zhipu AI · Open weights 1,428 ±23 78 +105◆ A ›
36 B Qwen3.5 397B A17Bprovisional Alibaba · Open weights 1,426 ±14 76 +27 A ›
37 B Kimi K2.6provisional Moonshot AI · Open weights 1,425 ±17 75 +44 A ›
40 B GLM 4.6provisional Zhipu AI · Open weights 1,423 ±18 73 +26 A ›
43 B Kimi K2.5provisional Moonshot AI · Open weights 1,420 ±14 71 +36 A ›
44 B Inklingprovisional Thinking Machines Lab · Open weights 1,420 ±20 70 +26 A ›
46 B GLM 5provisional Zhipu AI · Open weights 1,420 ±20 68 +23 A ›
56 B MiniMax M3provisional MiniMax · Open weights 1,407 ±16 62 +50 A ›
59 B Qwen3.5-122B-A10Bprovisional Alibaba · Open weights 1,403 ±22 59 +14 A ›
60 B MiMo-V2.5provisional Xiaomi · Open weights 1,402 ±17 59 +75 A ›
62 B DeepSeek V4 Flash 0423provisional DeepSeek · Open weights 1,401 ±17 57 +71 A ›
66 B Qwen3 Next 80B A3B Instructprovisional Alibaba · Open weights 1,397 ±21 54 +32 A ›
70 B DeepSeek V3.1provisional DeepSeek · Open weights 1,391 ±23 52 +19 A ›
71 B Step 3.5 Flashprovisional StepFun · Open weights 1,391 ±15 51 +50 A ›
72 B GLM 4.5provisional Zhipu AI · Open weights 1,389 ±20 50 +18 A ›
74 C Inkling Smallprovisional Thinking Machines Lab · Open weights 1,387 ±22 49 +38 A ›
75 C MiniMax M2.7provisional MiniMax · Open weights 1,384 ±14 48 +32 A ›
83 C Qwen3.5-27Bprovisional Alibaba · Open weights 1,378 ±21 43 +14 A ›
88 C Qwen3 30B A3B Instruct 2507provisional Alibaba · Open weights 1,368 ±20 39 +25 A ›
89 C GLM 4.5 Airprovisional Zhipu AI · Open weights 1,367 ±17 38 -2 A ›
92 C Qwen3.5-35B-A3Bprovisional Alibaba · Open weights 1,358 ±20 36 +11 A ›
94 C MiniMax M2.5provisional MiniMax · Open weights 1,354 ±19 35 +9 A ›
95 C gpt-oss-120bprovisional OpenAI · Open weights 1,349 ±18 34 -2 A ›
96 C Trinity Large Thinkingprovisional Arcee AI · Open weights 1,349 ±22 34 -30 A ›
99 C MiniMax M1provisional MiniMax · Open weights 1,346 ±16 32 -36 A ›
101 C Mistral Small 3.2provisional Mistral AI · Open weights 1,337 ±23 30 -14 A ›
104 C Qwen3 Coder 480B A35Bprovisional Alibaba · Open weights 1,325 ±20 28 -22 A ›
105 C gemma-3n-e4b-itprovisional Google · Open weights 1,316 ±17 27 — A ›
108 C Qwen3 30B A3Bprovisional Alibaba · Open weights 1,313 ±17 25 -29 A ›
125 D mixtral-8x22b-instruct-v0.1provisional Mistral AI · Open weights 1,181 ±14 13 -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.