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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.
Maple-PreviewGrok 4.6Gemini 3.8 Flash
Focus

Hard reasoning: quality vs price

Compared models are ringed; the other 190 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, GPT-6 Luna, Qwen3.8 Flash-Next, GLM-5.3 Flash, MiMo-V2.6-Pro, Muse Spark 1.3, Claude Sonnet 5.5, Claude Opus 5.5. 80 models are not plotted: Ternary Bonsai 2 27B, DeepSeek V4.1 Flash NVFP4, GLM-5.3 Flash NVFP4, GLM-5.3 NVFP4, Qwen3.8 Flash-Next NVFP4, Qwen3.8 27B NVFP4, Motif 3, Nex-N2-Pro, Ternary Bonsai 27B, K2 Horizon 375B A23B, A.X-K2, Solar Open2 250B, Bonsai 27B, Xing4.0-29B-A4B, K2-Horizon-32B, K2 Horizon MoVA 36B A4B, 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, Command A+, K2 Horizon 3.7B, Ling 3.0 Tiny, K2 Think V2, Step3 VL 10B, DiffusionGemma 26B A4B, North Mini Code, K2-V2, Falcon-H1R-7B, 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, NVIDIA Nemotron 3 Nano 4B, Devstral Small 2, 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, Granite 4.0 Micro, 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.

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

Ternary Bonsai 2 27B, DeepSeek V4.1 Flash NVFP4, GLM-5.3 Flash NVFP4, GLM-5.3 NVFP4, Qwen3.8 Flash-Next NVFP4, Qwen3.8 27B NVFP4, Motif 3, Nex-N2-Pro, Ternary Bonsai 27B, K2 Horizon 375B A23B, A.X-K2, Solar Open2 250B, Bonsai 27B, Xing4.0-29B-A4B, K2-Horizon-32B, K2 Horizon MoVA 36B A4B, 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, Command A+, K2 Horizon 3.7B, Ling 3.0 Tiny, K2 Think V2, Step3 VL 10B, DiffusionGemma 26B A4B, North Mini Code, K2-V2, Falcon-H1R-7B, 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, NVIDIA Nemotron 3 Nano 4B, Devstral Small 2, 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, Granite 4.0 Micro, 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
  • Maple-Preview
    Quality
    53.7
    Rank
    #68/193
    Price
    —
    Speed
    —

    vs Gemini 3.8 Flash: −28.9 quality · price n/a

  • Grok 4.6
    Quality
    81.8
    Rank
    #14/193
    Price
    $3.00/M tok
    Speed
    60tok/s
    In$2.00Out$6.00/M tok

    vs Gemini 3.8 Flash: −0.8 quality · 2× price

  • Gemini 3.8 Flash
    Quality
    82.6
    Rank
    #12/193
    Price
    $1.50/M tok
    Speed
    291tok/s
    In$0.75▾Out$3.75▾/M tok

    vs Grok 4.6: +0.8 quality · 0.5× price

Overview

Lab
DeepGrove
SpaceXAI (xAI)
Google DeepMind
Released
Aug 4, 2026
Aug 12, 2026
Sep 2, 2026
Weights
Open
Proprietary
Proprietary
Input price
$/M tok
—
$2.00
$0.75Best
Output price
$/M tok
—
$6.00
$3.75Best
Blended price
$/M tok · 3:1 in:out
—
$3.00
$1.50Best
Output speed
tok/s
—
60
291Best
Context
tokens
131K
500K
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.

57.4±10.3
#32/114Lab-reported
57.5±4.5Best
#31/114Verified
57.1±4.5
#33/114Verified
Not ranked
71.2±6.0
#29/155Verified
76.7±4.7Best
#22/155Verified
Not ranked
64.4±7.3
#27/90Verified
82.6±4.2Best
#6/90Mixedbest value
53.7±7.5
#68/193Lab-reported
81.9±3.7
#14/193Verified
82.6±3.7Best
#12/193Verified
Not ranked
84.0±5.8Best
#11/176Verified
76.1±5.8
#17/176Verified

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.

Writing & chat benchmarks

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

IFBench
day 0
—
~#29/134~71.4%Estimated
estimated from related benchmarks
MMMLU
day 0
—
~#2/5~89.6%Estimated
estimated from related benchmarks
~#2/5~92.0%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
~#2/3~71.3%Estimated
estimated from related benchmarks
—
~#4/8~88.2%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.

—
~#2/117~96.9%Estimated
estimated from related benchmarks
~#2/117~98.3%Estimated
estimated from related benchmarks
—
~#3/12~82.4%Estimated
estimated from related benchmarks