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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 Flash-NextDeepSeek V4.1 FlashGPT-6 Astra
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
  • Qwen3.8 Flash-Next
    Quality
    62.0
    Rank
    #20/103
    Price
    $0.23/M tok
    Speed
    54tok/s
    In$0.15▾Out$0.47▾/M tok

    vs GPT-6 Astra: −24.3 quality · 0.01× price

  • DeepSeek V4.1 Flash
    Quality
    66.4
    Rank
    #14/103
    Price
    $0.52/M tok
    Speed
    232tok/s
    In$0.30Out$1.20/M tok

    vs GPT-6 Astra: −19.8 quality · 0.03× price

  • GPT-6 Astra
    Quality
    86.2
    Rank
    #4/103
    Price
    $20.0/M tok
    Speed
    52tok/s
    In$10Out$50/M tok

    vs DeepSeek V4.1 Flash: +19.8 quality · 38× price

Overview

Lab
Alibaba Qwen
DeepSeek
OpenAI
Released
Aug 26, 2026
Sep 10, 2026
Sep 3, 2026
Weights
Open
Open
Proprietary
Input price
$/M tok
$0.15Best
$0.30
$10
Output price
$/M tok
$0.47Best
$1.20
$50
Blended price
$/M tok · 3:1 in:out
$0.23Best
$0.525
$20
Output speed
tok/s
54
232Best
52
Context
tokens
262K
1MBest
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.

62.0±5.2
#20/103Mixed
66.4±6.4
#14/103Mixedbest value
86.2±4.5Best
#4/103Verified
80.0±9.7
#12/146Lab-reportedbest value
69.8±4.3
#26/146Verified
92.1±5.3Best
#4/146Verified
72.6±5.3
#16/82Mixed
56.4±4.8
#35/82Verified
98.9±3.0Best
#1/82Verifiedbest value
76.0±3.7
#24/182Verifiedbest value
76.1±4.0
#23/182Mixed
93.5±3.7Best
#4/182Verified
81.7±5.8
#14/173Verifiedbest value
82.1±7.8Best
#13/173Verified
81.6±4.6
#15/173Mixed

Coding benchmarks

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

MMMLU
day 0
~#2/5~91.5%Estimated
estimated from related benchmarks
~#2/5~90.8%Estimated
estimated from related benchmarks
~#2/5~91.4%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.

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
~#2/25~96.9%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.

Not comparable across labs (2)