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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.
Claude Opus 5.5Granite 4.2 3BQwen3.8 Flash-Next
Focus

Coding: quality vs price

Compared models are ringed; the other 100 ranked here are greyed.

Lower price is better. Pareto frontier: Granite 4.2 3B, NVIDIA Nemotron 3 Nano 30B A3B, gpt-oss-20b, Ling-3.0-flash-VL, MiMo-V2.6-Flash, GLM-5.3 Flash, DeepSeek V4.1 Flash, MiMo-V2.6-Pro, Claude Sonnet 5.5, Claude Opus 5.5. 26 models are not plotted: Xing4.0-29B-A4B, Nex-N2.5-Pro, Atria Dawn Preview, Intern-S2-397B, Solar Open2 250B, Phi-4-reasoning-plus, Motif 3, Nex-N2-Pro, Nex-N2.5-Mini, A.X-K2, K2 Horizon 375B A23B, K-EXAONE 2.0 0803, K2 Horizon MoVA 36B A4B, Intern-S2-Preview (35B-A3B), Command A+, North Mini Code, G9v3-39A5B, Falcon-H1-34B-Instruct, Devstral 2, K2 Horizon 7B, Devstral Small 2, MiniCPM5-2B, Ling 3.0 Tiny, K2 Horizon 3.7B, K2 Horizon 0.9B, LFM2.5-2.6B.

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

Xing4.0-29B-A4B, Nex-N2.5-Pro, Atria Dawn Preview, Intern-S2-397B, Solar Open2 250B, Phi-4-reasoning-plus, Motif 3, Nex-N2-Pro, Nex-N2.5-Mini, A.X-K2, K2 Horizon 375B A23B, K-EXAONE 2.0 0803, K2 Horizon MoVA 36B A4B, Intern-S2-Preview (35B-A3B), Command A+, North Mini Code, G9v3-39A5B, Falcon-H1-34B-Instruct, Devstral 2, K2 Horizon 7B, Devstral Small 2, MiniCPM5-2B, 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
  • Claude Opus 5.5
    Quality
    96.9
    Rank
    #1/103
    Price
    $8.00/M tok
    Speed
    92tok/s
    In$4.00Out$20/M tok

    vs Qwen3.8 Flash-Next: +34.9 quality · 35× price

  • Granite 4.2 3B
    Quality
    17.2
    Rank
    #95/103
    Price
    $0.05/M tok
    Speed
    220tok/s
    In$0.03▾Out$0.12▾/M tok

    vs Claude Opus 5.5: −79.6 quality · 0.01× price

  • Qwen3.8 Flash-Next
    Quality
    62.0
    Rank
    #20/103
    Price
    $0.23/M tok
    Speed
    54tok/s
    In$0.15Out$0.47/M tok

    vs Claude Opus 5.5: −34.9 quality · 0.03× price

Overview

Lab
Anthropic
IBM Granite
Alibaba Qwen
Released
Sep 22, 2026
Aug 25, 2026
Aug 26, 2026
Weights
Proprietary
Open
Open
Input price
$/M tok
$4.00
$0.03Best
$0.15
Output price
$/M tok
$20
$0.12Best
$0.47
Blended price
$/M tok · 3:1 in:out
$8.00
$0.052Best
$0.23
Output speed
tok/s
92
220Best
54
Context
tokens
1MBest
131K
262K

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.

96.9±3.4Best
#1/103Mixedbest value
17.2±8.0
#95/103Verifiedbest value
62.0±5.2
#20/103Mixed
93.0±4.3Best
#3/146Verifiedbest value
Not ranked
80.0±9.7
#12/146Lab-reportedbest value
94.5±4.8Best
#2/82Verifiedbest value
Not ranked
72.6±5.3
#16/82Mixed
99.9±2.6Best
#1/182Verifiedbest value
23.4±3.7
#128/182Verified
76.0±3.7
#24/182Verifiedbest value
99.6±4.1Best
#1/173Verifiedbest value
17.0±5.8
#140/173Verified
81.7±5.8
#14/173Verifiedbest value

Coding benchmarks

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

~last/15below measured range (<41.1)Estimated
estimated from related benchmarks
~#10/15~47.7Estimated
estimated from related benchmarks
Not comparable across labs (1)
Benchmarks in other categories (4)

Writing & chat benchmarks

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

MMMU
day 0
~#2/3~71.3%Estimated
estimated from related benchmarks
—
~#2/8~91.4%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.

~#2/25~96.7%Estimated
estimated from related benchmarks
~#2/25~96.7%Estimated
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.

~#2/116~98.3%Estimated
estimated from related benchmarks
~#2/116~98.3%Estimated
estimated from related benchmarks
~#2/12~84.6%Estimated
estimated from related benchmarks
~#2/5~58.7%Estimated
estimated from related benchmarks
~last/5below measured range (<47.5%)Estimated
estimated from related benchmarks
Not comparable across labs (2)