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Model Pareto

Muse Wonder Full vs Kimi K3.1 Pro

Muse Wonder Full scores higher in vision and hard reasoning; Kimi K3.1 Pro in coding. Muse Wonder Full has no listed API price; Kimi K3.1 Pro costs $4.00 per million tokens, blended 3:1 input:output.
Muse Wonder FullKimi K3.1 Pro
Focus

Coding: quality vs price

Compared models are ringed; the other 131 ranked here are greyed.

Lower price is better. Pareto frontier: Nex-N2.5-Mini, Nex-N2.5-Pro, MiMo-V2.6-Flash, Ternary Bonsai 2 27B, MiMo-V2.6-Pro, Kimi K3.1 Pro, Claude Opus 5.5. 43 models are not plotted: Muse Wonder Full, GLM-5.3 Flash NVFP4, DeepSeek V4.1 Flash NVFP4, DeepSeek-V4-Pro-0813-nvfp4-DSpark, GLM-5.3 NVFP4, Atria Dawn Preview, Ternary Bonsai 27B, Xing4.0-29B-A4B, Beam 1.0 501B A23B, Qwen3.8 27B NVFP4, Maple-Preview, Intern-S2-397B, Bonsai 27B, Solar Open2 250B, Muse Spark Open, Laguna S 2.1, Phi-4-reasoning-plus, Motif 3, Nex-N2-Pro, Flint, Nemotron 3.5 Super VL NVFP4, Nemotron 3.5 Super VL, K2 Horizon 375B A23B, K-EXAONE 2.0 0803, A.X-K2, Intern-S2-Preview (35B-A3B), Nemotron 3.5 Bolt 74B-A9B, K2 Horizon MoVA 36B A4B, G9v3-39A5B, North Mini Code, Qwen3.8 Flash-Next NVFP4, LFM2.5-24B-A2B, K2 Horizon 7B, K2-Horizon-32B, Devstral 2, NVIDIA Nemotron-Labs Dory1.0 4B, MiniCPM5-2B, Falcon-H1-34B-Instruct, Devstral Small 2, Ling 3.0 Tiny, K2 Horizon 3.7B, K2 Horizon 0.9B, LFM2.5-2.6B.

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

Muse Wonder Full, GLM-5.3 Flash NVFP4, DeepSeek V4.1 Flash NVFP4, DeepSeek-V4-Pro-0813-nvfp4-DSpark, GLM-5.3 NVFP4, Atria Dawn Preview, Ternary Bonsai 27B, Xing4.0-29B-A4B, Beam 1.0 501B A23B, Qwen3.8 27B NVFP4, Maple-Preview, Intern-S2-397B, Bonsai 27B, Solar Open2 250B, Muse Spark Open, Laguna S 2.1, Phi-4-reasoning-plus, Motif 3, Nex-N2-Pro, Flint, Nemotron 3.5 Super VL NVFP4, Nemotron 3.5 Super VL, K2 Horizon 375B A23B, K-EXAONE 2.0 0803, A.X-K2, Intern-S2-Preview (35B-A3B), Nemotron 3.5 Bolt 74B-A9B, K2 Horizon MoVA 36B A4B, G9v3-39A5B, North Mini Code, Qwen3.8 Flash-Next NVFP4, LFM2.5-24B-A2B, K2 Horizon 7B, K2-Horizon-32B, Devstral 2, NVIDIA Nemotron-Labs Dory1.0 4B, MiniCPM5-2B, Falcon-H1-34B-Instruct, Devstral Small 2, Ling 3.0 Tiny, K2 Horizon 3.7B, K2 Horizon 0.9B, LFM2.5-2.6B

  • Best-value frontier (nothing is both cheaper and better)
  • Evidencestrong → weak
  • Estimated from other categories
  • Muse Wonder Full
    Quality
    91.7
    Rank
    #3/133
    Price
    —
    Speed
    —

    vs Kimi K3.1 Pro: −1.3 quality · price n/a

  • Kimi K3.1 Pro
    Quality
    92.9
    Rank
    #2/133
    Price
    $4.00/M tok
    Speed
    77tok/s
    In$2.00Out$10/M tok

    vs Muse Wonder Full: +1.3 quality · price n/a

Overview

Lab
Meta
Moonshot AI (Kimi)
Released
Oct 12, 2026
Oct 23, 2026
Weights
Proprietary
Proprietary
Input price
$/M tok
—
$2.00
Output price
$/M tok
—
$10
Blended price
$/M tok · 3:1 in:out
—
$4.00
Output speed
tok/s
—
77
Context
tokens
1M
1MBest

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.

91.7±4.5
#3/133Verified
92.9±7.6Best
#2/133Verifiedbest value
94.4±7.5Best
#2/97Verified
82.3±8.2
#8/97Verified
94.4±5.1Best
#5/213Verified
93.3±5.1
#7/213Verified
99.0±4.4
#2/186Verified
Not ranked

Coding benchmarks

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

~#2/20~93.9%Estimated
estimated from related benchmarks
~#2/20~92.6%Estimated
estimated from related benchmarks
LiveCodeBench
third-party
~#2/28~92.1%Estimated
estimated from related benchmarks
Benchmarks in other categories (3)

Vision benchmarks

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

MMMU
day 0
~#2/3~71.3%Estimated
estimated from related benchmarks
~#2/3~71.3%Estimated
estimated from related benchmarks
~#2/12~86.0%Estimated
estimated from related benchmarks
~#2/12~85.1%Estimated
estimated from related benchmarks
~#2/8~91.1%Estimated
estimated from related benchmarks

Hard reasoning 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.