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MODEL CAPABILITY · PRICE & RELEASES

Capabilities

Artificial Analysis · Compare benchmark results, text costs and release dates separately, with each model and configuration identified.

Data as of2026-10-27 · 272 configurations

Methodology & original report ↗

Capability comparison

One capability index · top configuration per developer · excludes estimates, retired models and future releases.

Chart data · 10 entries
NameCapability index
Claude Opus 5.557.622
Muse Wonder Full55.802
Kimi K3.1 Pro55.087
GPT-6 Astra52.674
Gemini 4 Argon52.561
Grok 4.746.447
MiMo-V2.6-Pro46.324
Qwen3.8 Max (0902)45.415
DeepSeek V4.1 Flash 101745.097
GLM-5.344.777

Capability and text cost

Index ≥30 · cost = (3 × input price + output price) ÷ 4, in USD per million tokens. Showing 60 of 66 configurations, selected by capability.

Chart data · 60 entries
NameCost / capability
Claude Opus 5.5 (Max, Default Fallback)8 / 57.622
Claude Sonnet 5.5 (Max, Default Fallback)4 / 56
Claude Opus 5.5 (Xhigh, Default Fallback)8 / 55.987
Kimi K3.1 Pro4 / 55.087
Claude Opus 5.5 (High, Default Fallback)8 / 53.583
Claude Fable 5.1 (Max, Default Fallback)20 / 53.355
Claude Fable 5.1 (Xhigh, Default Fallback)20 / 53.203
GPT-6 Astra (Max)20 / 52.674
Gemini 4 Argon (High)4 / 52.561
Muse Wonder (Max)8 / 52.394
GPT-6 Astra (Xhigh)20 / 52.386
Claude Sonnet 5.5 (Xhigh, Default Fallback)4 / 51.896
GPT-6.1 Sol (Max)4 / 51.833
Claude Opus 5.5 (Medium, Default Fallback)8 / 51.243
Claude Fable 5.1 (High, Default Fallback)20 / 51.152
GPT-6.1 Sol (Xhigh)4 / 51.038
GPT-6 Astra (High)20 / 50.919
GPT-6.1 Sol (High)4 / 50.238
GPT-6 Astra (Medium)20 / 49.57
Claude Fable 5.1 (Medium, Default Fallback)20 / 48.921
GPT-6.1 Terra (Max)2 / 48.106
GPT-6.1 Sol (Medium)4 / 47.783
Claude Fable 5.1 (Low, Default Fallback)20 / 46.816
Claude Sonnet 5.5 (High, Default Fallback)4 / 46.753
Grok 4.7 (Xhigh)3 / 46.447
Grok 4.7 (High)3 / 46.332
MiMo-V2.6-Pro0.544 / 46.324
GPT-6 Astra (Low)20 / 45.782
Qwen3.8 Max (0902)3 / 45.415
GPT-6.1 Terra (Xhigh)2 / 45.364
DeepSeek V4.1 Flash 1017 (Max)0.525 / 45.097
GLM-5.3 (Max)2.15 / 44.777
Ling 3.1 Flash 10190.45 / 43.897
GPT-6.1 Terra (High)2 / 43.869
Step 5 Preview1.425 / 43.734
Claude Haiku 5.5 (Max)0.2 / 43.395
MiniMax-M3.1-Flash0.262 / 42.706
Claude Opus 5.5 (Low, Default Fallback)8 / 42.308
Grok 4.7 (Low)3 / 42.217
GPT-6.1 Sol (Low)4 / 42.084
GLM-5.3-Flash0.238 / 41.807
GPT-6.1 Terra (Medium)2 / 41.319
Claude Haiku 5.5 (Xhigh)0.2 / 41.249
Ling 3.1 Flash0.45 / 41.091
Gemini 3.8 Flash (High)1.5 / 40.926
Claude Sonnet 5.5 (Medium, Default Fallback)4 / 40.839
Qwen3.8 2.4T A95B3 / 39.946
Qwen3.8-Flash-Next0.23 / 39.822
Gemini 3.8 Flash (Medium)1.5 / 39.774
Muse Spark Open (Xhigh)2 / 38.867
Mistral Large 4 Preview2.065 / 38.377
GPT-6 Luna (Max)0.2 / 38.125
MiMo-V2.6-Flash0.175 / 37.884
Claude Haiku 5.5 (High)0.2 / 37.824
GPT-6.1 Terra (Low)2 / 36.213
DeepSeek V4 Pro 0813 (Max)1.98 / 35.997
Claude Sonnet 5.5 (Low, Default Fallback)4 / 35.868
DeepSeek V4 Flash Vision (Max)0.66 / 34.839
GPT-6 Luna (Xhigh)0.2 / 34.559
Claude Haiku 5.5 (Medium)0.2 / 34.465

CapabilitiesDetails

Sorted by capability index, with reasoning configurations kept separate. Estimates and historical records follow rank-eligible entries.

On this page20records
  1. —

    Qwen3.5 397B A17B (Non-reasoning)

    Alibaba · Estimated score

    Capability index
    21.44
    Input / million tokens
    $0.6
    Output / million tokens
    $3.6
    Value · 3:1 usage mix
    —
  2. —

    Qwen3.5 Omni Plus

    Alibaba · Estimated score

    Capability index
    20.38
    Input / million tokens
    $0.4
    Output / million tokens
    $4.8
    Value · 3:1 usage mix
    —
  3. —

    o3

    OpenAI · Estimated score

    Capability index
    20.20
    Input / million tokens
    $2
    Output / million tokens
    $8
    Value · 3:1 usage mix
    —
  4. —

    K-EXAONE 2.0 0803

    LG AI Research · Estimated score

    Capability index
    19.69
    Input / million tokens
    —
    Output / million tokens
    —
    Value · 3:1 usage mix
    —
  5. —

    JT-35B-Flash

    China Mobile · Estimated score

    Capability index
    18.66
    Input / million tokens
    —
    Output / million tokens
    —
    Value · 3:1 usage mix
    —
  6. —

    Qwen3.5 122B A10B (Non-reasoning)

    Alibaba · Estimated score

    Capability index
    17.75
    Input / million tokens
    $0.4
    Output / million tokens
    $3.2
    Value · 3:1 usage mix
    —
  7. —

    Doubao Seed Code

    ByteDance Seed · Estimated score

    Capability index
    16.95
    Input / million tokens
    —
    Output / million tokens
    —
    Value · 3:1 usage mix
    —
  8. —

    Gemma 4 26B A4B (Reasoning)

    Google · Estimated score

    Capability index
    16.67
    Input / million tokens
    $0.1
    Output / million tokens
    $0.34
    Value · 3:1 usage mix
    —
  9. —

    Qwen3.6 35B A3B (Non-reasoning)

    Alibaba · Estimated score

    Capability index
    15.23
    Input / million tokens
    $0.375
    Output / million tokens
    $2.25
    Value · 3:1 usage mix
    —
  10. —

    ERNIE 5.0 Thinking Preview

    Baidu · Estimated score

    Capability index
    14.26
    Input / million tokens
    —
    Output / million tokens
    —
    Value · 3:1 usage mix
    —
  11. —

    Gemma 4 12B (Reasoning)

    Google · Estimated score

    Capability index
    14.18
    Input / million tokens
    $0.1
    Output / million tokens
    $0.3
    Value · 3:1 usage mix
    —
  12. —

    Nova 2.0 Pro Preview (Medium)

    Amazon · Estimated score

    Capability index
    14.16
    Input / million tokens
    $1.25
    Output / million tokens
    $10
    Value · 3:1 usage mix
    —
  13. —

    Gemma 4 31B (Non-reasoning)

    Google · Estimated score

    Capability index
    13.92
    Input / million tokens
    $0.165
    Output / million tokens
    $0.4
    Value · 3:1 usage mix
    —
  14. —

    Nova 2.0 Omni (Medium)

    Amazon · Estimated score

    Capability index
    13.65
    Input / million tokens
    —
    Output / million tokens
    —
    Value · 3:1 usage mix
    —
  15. —

    Apriel-v1.6-15B-Thinker

    ServiceNow · Estimated score

    Capability index
    13.40
    Input / million tokens
    —
    Output / million tokens
    —
    Value · 3:1 usage mix
    —
  16. —

    Nova 2.0 Lite (High)

    Amazon · Estimated score

    Capability index
    13.37
    Input / million tokens
    $0.3
    Output / million tokens
    $2.5
    Value · 3:1 usage mix
    —
  17. —

    Qwen3.5 9B (Non-reasoning)

    Alibaba · Estimated score

    Capability index
    13.27
    Input / million tokens
    $0.17
    Output / million tokens
    $0.25
    Value · 3:1 usage mix
    —
  18. —

    EXAONE 4.5 33B (Reasoning)

    LG AI Research · Estimated score

    Capability index
    13.21
    Input / million tokens
    —
    Output / million tokens
    —
    Value · 3:1 usage mix
    —
  19. —

    Gemma 4 26B A4B (Non-reasoning)

    Google · Estimated score

    Capability index
    13.13
    Input / million tokens
    $0.115
    Output / million tokens
    $0.4
    Value · 3:1 usage mix
    —
  20. —

    Qwen3.5 4B (Reasoning)

    Alibaba · Estimated score

    Capability index
    13.12
    Input / million tokens
    $0.03
    Output / million tokens
    $0.15
    Value · 3:1 usage mix
    —
Ranking criteria & test conditions
  • Intelligence scores come from the same leaderboard snapshot. Reasoning configurations remain separate; estimated scores and retired models are excluded from the highlights.
  • Release dates follow the report and are separate from our update dates. Undated models are excluded from recent releases. A report’s date is not treated as independent proof of an official model release.
  • Text prices use USD per million tokens, with input and output stored separately. Subscription, cache and media charges are not mixed into this comparison.
  • Value picks require an intelligence score of at least 30 and rank by score ÷ (3 × input price + output price) × 4, selecting the highest value for each developer. This assumes a 3:1 text input/output ratio; actual task costs vary.
  • Developer highlights show one representative per developer. Model aliases and reasoning configurations are not automatically mapped to a specific catalog version. Full results retain the original configurations and missing values.

Charts are calculated from the full report, independently of the detail page. Release activity uses release dates, not changes in model capability over time. Missing prices are not zero costs.