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Model Pareto

Compare models

Side by side: quality per category, price, speed, and every benchmark cell with where it came from. Pick up to four.
Qwen3.8 27B MXFP4Qwen3.7 PlusKimi K2.7 Code
Focus

Hard reasoning: quality vs price

Compared models are ringed; the other 197 ranked here are greyed.

Lower price is better. Pareto frontier: Gemma 4 E4B, Sarvam 30B, Qwen3.5 4B, Sarvam 105B, Ling-3.0-flash-VL, MiMo-V2.5, Ternary Bonsai 2 27B, Claude Haiku 5.5, MiMo-V2.6-Pro, Muse Spark 1.3, Claude Sonnet 5.5, Claude Opus 5.5. 81 models are not plotted: DeepSeek V4.1 Flash NVFP4, GLM-5.3 Flash NVFP4, GLM-5.3 NVFP4, Qwen3.8 Flash-Next NVFP4, DeepSeek-V4-Pro-0813-nvfp4-DSpark, Kimi-K3-INT4, Qwen3.8 27B NVFP4, Motif 3, Nex-N2-Pro, GLM-5.3-MXFP4, Ternary Bonsai 27B, K2 Horizon 375B A23B, Qwen3.8 27B MXFP4, Solar Open2 250B, Bonsai 27B, Xing4.0-29B-A4B, A.X-K2, K2 Horizon MoVA 36B A4B, K2-Horizon-32B, Maple-Preview, Intern-S2-Preview (35B-A3B), K-EXAONE 2.0 0803, G9v3-39A5B, K2 Horizon 7B, Phi-4-reasoning-plus, EXAONE 4.5 33B, Nanbeige4.1-3B, HyperNova 60B 2605, Nemotron Cascade 2 30B A3B, INTELLECT-3, Apriel-v1.6-15B-Thinker, K2 Horizon 3.7B, K2 Think V2, Step3 VL 10B, DiffusionGemma 26B A4B, North Mini Code, K2-V2, Falcon-H1R-7B, Ling 3.0 Tiny, Solar Open 100B, Llama 3.1 Nemotron Ultra 253B v1, MiniCPM5-2B, LongCat Flash Lite, HyperCLOVA X SEED Think (32B), Tri-21B-Think, EXAONE 4.0 32B, Olmo 3.1 32B Think, LFM2.5-2.6B, Devstral 2, LFM2.5-8B-A1B, Olmo 3.1 32B Instruct, Olmo 3 7B Think, Devstral Small 2, NVIDIA Nemotron 3 Nano 4B, Phi-4-mini-flash-reasoning, Qwen3.5 2B, Falcon-H1-34B-Instruct, Hermes 4 - Llama-3.1 70B, LFM2 24B A2B, Exaone 4.0 1.2B, Llama 3.2 Instruct 90B (Vision), Gemma 4 E2B, Molmo2-8B, LFM2.5-1.2B-Thinking, LFM2.5-1.2B-Instruct, MiniCPM5-1B, Jamba 1.7 Large, MiniCPM-V 4.6 1.3B, Kimi Linear 48B A3B Instruct, Jamba Reasoning 3B, Jamba 1.7 Mini, Phi-4 Multimodal Instruct, Tiny Aya Global, LFM2.5-VL-1.6B, Granite 4.0 H 350M, K2 Horizon 0.9B, Qwen3.5 0.8B, Granite 4.0 350M, Granite 4.0 H 1B, Molmo 7B-D, Gemma 3 270M.

81 models with no price data — shown in the strip at the left edge

DeepSeek V4.1 Flash NVFP4, GLM-5.3 Flash NVFP4, GLM-5.3 NVFP4, Qwen3.8 Flash-Next NVFP4, DeepSeek-V4-Pro-0813-nvfp4-DSpark, Kimi-K3-INT4, Qwen3.8 27B NVFP4, Motif 3, Nex-N2-Pro, GLM-5.3-MXFP4, Ternary Bonsai 27B, K2 Horizon 375B A23B, Qwen3.8 27B MXFP4, Solar Open2 250B, Bonsai 27B, Xing4.0-29B-A4B, A.X-K2, K2 Horizon MoVA 36B A4B, K2-Horizon-32B, Maple-Preview, Intern-S2-Preview (35B-A3B), K-EXAONE 2.0 0803, G9v3-39A5B, K2 Horizon 7B, Phi-4-reasoning-plus, EXAONE 4.5 33B, Nanbeige4.1-3B, HyperNova 60B 2605, Nemotron Cascade 2 30B A3B, INTELLECT-3, Apriel-v1.6-15B-Thinker, K2 Horizon 3.7B, K2 Think V2, Step3 VL 10B, DiffusionGemma 26B A4B, North Mini Code, K2-V2, Falcon-H1R-7B, Ling 3.0 Tiny, Solar Open 100B, Llama 3.1 Nemotron Ultra 253B v1, MiniCPM5-2B, LongCat Flash Lite, HyperCLOVA X SEED Think (32B), Tri-21B-Think, EXAONE 4.0 32B, Olmo 3.1 32B Think, LFM2.5-2.6B, Devstral 2, LFM2.5-8B-A1B, Olmo 3.1 32B Instruct, Olmo 3 7B Think, Devstral Small 2, NVIDIA Nemotron 3 Nano 4B, Phi-4-mini-flash-reasoning, Qwen3.5 2B, Falcon-H1-34B-Instruct, Hermes 4 - Llama-3.1 70B, LFM2 24B A2B, Exaone 4.0 1.2B, Llama 3.2 Instruct 90B (Vision), Gemma 4 E2B, Molmo2-8B, LFM2.5-1.2B-Thinking, LFM2.5-1.2B-Instruct, MiniCPM5-1B, Jamba 1.7 Large, MiniCPM-V 4.6 1.3B, Kimi Linear 48B A3B Instruct, Jamba Reasoning 3B, Jamba 1.7 Mini, Phi-4 Multimodal Instruct, Tiny Aya Global, LFM2.5-VL-1.6B, Granite 4.0 H 350M, K2 Horizon 0.9B, Qwen3.5 0.8B, Granite 4.0 350M, Granite 4.0 H 1B, Molmo 7B-D, Gemma 3 270M

  • Best-value frontier (nothing is both cheaper and better)
  • Evidencestrong → weak
  • Estimated from other categories
  • Qwen3.8 27B MXFP4
    Quality
    65.2
    Rank
    #49/200
    Price
    —
    Speed
    —

    vs Qwen3.7 Plus: ±0.0 quality · price n/a

  • Qwen3.7 Plus
    Quality
    65.2
    Rank
    #48/200
    Price
    $0.70/M tok
    Speed
    65tok/s
    In$0.40▾Out$1.60▾/M tok

    vs Qwen3.8 27B MXFP4: ±0.0 quality · price n/a

  • Kimi K2.7 Code
    Quality
    65.2
    Rank
    #50/200
    Price
    $1.71/M tok
    Speed
    90tok/s
    In$0.95Out$4.00/M tok

    vs Qwen3.8 27B MXFP4: ±0.0 quality · price n/a

Overview

Lab
Red Hat AI
Alibaba Qwen
Moonshot AI (Kimi)
Released
Sep 16, 2026
Jun 1, 2026
Jun 12, 2026
Weights
Open
Proprietary
Open
Input price
$/M tok
—
$0.40Best
$0.95
Output price
$/M tok
—
$1.60Best
$4.00
Blended price
$/M tok · 3:1 in:out
—
$0.70Best
$1.712
Output speed
tok/s
—
65
90Best
Context
tokens
262K
1MBest
256K

Quality by category

0–100 within each category (100 = best tracked model). Rank is among all models ranked there. Click a row to focus it.

Not ranked
37.1±8.0
#60/119Verified
37.8±8.0Best
#58/119Verified
Not ranked
70.3±4.1Best
#27/156Verified
54.6±9.5
#60/156Verified
Not ranked
62.7±6.2
#33/91Verified
Not ranked
65.2±8.3
#49/200Lab-reported
65.2±3.7Best
#48/200Verified
65.2±3.7
#50/200Verified
Not ranked
42.4±4.2
#58/178Verified
45.4±4.2Best
#49/178Verified

Hard reasoning benchmarks

Rank among models with a published score, and the leaderboard around each model. ~ italic = no published score, estimated from related benchmarks.

Benchmarks in other categories (4)

Coding benchmarks

Rank among models with a published score, and the leaderboard around each model. ~ italic = no published score, estimated from related benchmarks.

—
~#15/20~55.7%Estimated
estimated from related benchmarks
~#15/20~56.0%Estimated
estimated from related benchmarks
—
~last/18below measured range (<41.1)Estimated
estimated from related benchmarks
~last/18below measured range (<41.1)Estimated
estimated from related benchmarks

Writing & chat benchmarks

Rank among models with a published score, and the leaderboard around each model. ~ italic = no published score, estimated from related benchmarks.

MMMLU
day 0
—
~#2/5~89.1%Estimated
estimated from related benchmarks

Vision benchmarks

Rank among models with a published score, and the leaderboard around each model. ~ italic = no published score, estimated from related benchmarks.

Agents benchmarks

Rank among models with a published score, and the leaderboard around each model. ~ italic = no published score, estimated from related benchmarks.

—
~#10/12~68.8%Estimated
estimated from related benchmarks
~#10/12~69.7%Estimated
estimated from related benchmarks
—
~last/5below measured range (<47.5%)Estimated
estimated from related benchmarks
~last/5below measured range (<47.5%)Estimated
estimated from related benchmarks