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text model comparison

GPT-5.6 Sol vs GPT-6 Luna

GPT-5.6 Sol and GPT-6 Luna are both current text models, from OpenAI. GPT-6 Luna is 98% cheaper per 1M input tokens than GPT-5.6 Sol, $0.10 vs $4. On Arena Text, GPT-5.6 Sol scores 1485 Elo and GPT-6 Luna 1443 Elo (Arena, measured October 8, 2026). Both accept 1,050,000 tokens of context.

Higher Arena Text
GPT-5.6 Sol1485 Elo against 1443 Elo
Lower price per 1M input tokens
GPT-6 Luna$0.10 against $4
Newer release
GPT-6 LunaSep 2026 against Jul 2026

Data verified October 13, 2026 · Dataset 2026-10-28

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Side by side

GPT-5.6 Sol and GPT-6 Luna, line by line

Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.

FactGPT-5.6 SolGPT-6 Luna
Price
Price per 1M input tokens$4$0.10 (better)
Price per 1M output tokens$20$0.50 (better)
Independent benchmarks
Arena Text1485 Elo (#11 of 49) (better)1443 Elo (#37 of 49)
GPQA Diamond93.5% (#11 of 40) (better)90.5% (#25 of 40)
FrontierMath Tiers 1-3 (v2)89.1% (#6 of 40) (better)78.9% (#14 of 40)
SimpleQA Verified69.7% (#7 of 36) (better)41.4% (#26 of 36)
Specs
ProviderOpenAIOpenAI
Release dateJuly 9, 2026September 22, 2026
AccessAPI onlyAPI only
Context window1,050,000 tokens1,050,000 tokens
Max output128,000 tokens128,000 tokens
Inputtext, imagetext, image
Outputtexttext

Scores:Arena · 2026-10-12Epoch AI · 2026-10-12

Open both in the interactive comparison

Charts

Against every current text model

Where GPT-5.6 Sol and GPT-6 Luna land among the current models of their task, on every independent benchmark, on price and on context.

Where each stands

On Arena Text, GPT-5.6 Sol ranks #11 and GPT-6 Luna #37 of 49 current models. Each bar is the share of current models it beats: on score, price and context.

GPT-5.6 Sol and GPT-6 Luna among the current text models, one row per benchmark, price and context window.

Independent benchmarks

Arena Text

GPT-5.6 Sol:#11of 49

1,485 Elo · ahead of 79%

GPT-6 Luna:#37of 49

1,443 Elo · ahead of 23%

GPQA Diamond

GPT-5.6 Sol:#11of 40

93.5% · ahead of 74%

GPT-6 Luna:#25of 40

90.5% · ahead of 36%

FrontierMath Tiers 1-3 (v2)

GPT-5.6 Sol:#6of 40

89.1% · ahead of 87%

GPT-6 Luna:#14of 40

78.9% · ahead of 67%

SimpleQA Verified

GPT-5.6 Sol:#7of 36

69.7% · ahead of 80%

GPT-6 Luna:#26of 36

41.4% · ahead of 29%

Price and context

Price per 1M input tokens

GPT-5.6 Sol:#42of 51

$4 · cheaper than 14%

GPT-6 Luna:#1of 51

$0.10 · cheaper than 96%

Price per 1M output tokens

GPT-5.6 Sol:#42of 51

$20 · cheaper than 14%

GPT-6 Luna:#3of 51

$0.50 · cheaper than 92%

Context window

GPT-5.6 Sol:#1of 66

1.05M tokens · longer than 86%

GPT-6 Luna:#1of 66

1.05M tokens · longer than 86%

Quality against price

Arena Text · measured October 8, 2026

GPT-5.6 Sol scores 1,485 at $8; GPT-6 Luna scores 1,443 at $0.2 (blended price per 1M tokens). The line joins the current models no other beats on both price and Arena Text score; hover any point to name it.

Quality against priceArena Text score against price for 41 text models, GPT-5.6 Sol and GPT-6 Luna marked.1,2501,3001,3501,4001,4501,5001,550$0.30$1$3$10MOST ATTRACTIVE QUADRANTMedian price $3Median 1,475Blended price per 1M tokens (3 input : 1 output), log scaleArena Text (Elo)GPT-5.6 SolGPT-6 Luna
Quality against price: the data
Arena Text score against price for 41 text models, GPT-5.6 Sol and GPT-6 Luna marked.
ModelProviderBlended price per 1M tokensArena Text (Elo)
Gemini 4 ArgonGoogle$41,525
Claude Opus 5.5Anthropic$81,507
Claude Fable 5Anthropic$201,504
Claude Fable 5.1Anthropic$201,501
Gemini 3.8 FlashGoogle$1.501,497
Muse Spark 1.3Meta$21,494
Claude Opus 5Anthropic$101,490
Kimi K3Moonshot Kimi$61,488
Gemini 3.7 FlashGoogle$1.501,486
GPT-5.6 SolOpenAI$81,485
GPT-6.1 SolOpenAI$41,484
Qwen3.8-MaxAlibaba Qwen$31,483
GPT-5.5OpenAI$111,482
Gemini 3.6 FlashGoogle$31,482
Claude Opus 4.8Anthropic$101,481
MiMo-V2.6-ProXiaomi MiMo$0.541,480
GLM-5.3Z.ai$2.151,478
Claude Sonnet 5.5Anthropic$41,476
GPT-5.4OpenAI$5.631,475
GPT-6 AstraOpenAI$201,475
DeepSeek-V4.1-FlashDeepSeek$0.521,475
GLM-5.3-FlashZ.ai$0.241,475
Gemini 3 Flash PreviewGoogle$1.131,473
Grok 4.20xAI$1.561,472
GPT-5.6 TerraOpenAI$4.501,466
Grok 4.5xAI$31,466
DeepSeek-V4-ProDeepSeek$1.981,465
Claude Sonnet 5Anthropic$41,461
GPT-6 SolOpenAI$41,456
Gemini 3.5 Flash-LiteGoogle$0.851,455
Grok 4.6xAI$31,454
GPT-5.6 LunaOpenAI$0.451,452
MiMo-V2.6-FlashXiaomi MiMo$0.181,451
GPT-6 LunaOpenAI$0.201,443
Grok 4.7xAI$31,443
Grok 4.3xAI$1.561,442
Mistral Large 4Mistral AI$2.061,429
GPT-4.1OpenAI$3.501,415
Solar Pro 4Upstage$0.521,386
GPT-4.1 miniOpenAI$0.701,383
GPT-4.1 nanoOpenAI$0.181,322

Cost

What each costs

Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.

The same work, priced

For 10M input and 2M output tokens, GPT-5.6 Sol costs 40 times as much as GPT-6 Luna, at each provider's listed rates.

What GPT-5.6 Sol and GPT-6 Lunacost for the same work, at each provider's listed rates.
  • 10M input and 2M output tokens

    GPT-5.6 Sol costs 40× as much

    GPT-5.6 Sol
    $80
    GPT-6 Luna
    $2
  • The same, with 9M of the input tokens cached

    GPT-5.6 Sol costs 40× as much

    GPT-5.6 Sol
    $47.60
    GPT-6 Luna
    $1.19
Workload or rateGPT-5.6 SolGPT-6 Luna
Example workloads
10M input and 2M output tokens$80$2 (better)
The same, with 9M of the input tokens cached$47.60$1.19 (better)
Other listed rates
1M input tokens, cached$0.40$0.01 (better)
1M input tokens, prompts over 272K tokensNot listed$0.20
1M output tokens, prompts over 272K tokensNot listed$0.75

Verdict

When to choose each

GPT-5.6 Sol

Where it leads

  • Higher Arena Text score, 1485 Elo vs 1443 Elo
  • Higher GPQA Diamond score, 93.5% vs 90.5%
  • Higher FrontierMath Tiers 1-3 (v2) score, 89.1% vs 78.9%
  • Higher SimpleQA Verified score, 69.7% vs 41.4%

Best for

  • Frontier capability evaluations for complex API workflows
  • Long-horizon coding and research agents that use computer use or MCP tools

Watch out for

  • Not in the pinned LiveBench snapshot; its independent scores here come from Epoch AI's own GPQA Diamond, FrontierMath and SimpleQA Verified runs

GPT-6 Luna

Where it leads

  • Lower input and output prices, $0.10 and $0.50 vs $4 and $20 per 1M tokens
  • Cheaper cached input, $0.01 vs $0.40 per 1M tokens

Best for

  • High-volume summarisation, extraction and routing
  • Long-context budget work, where the 272k threshold is more forgiving than Haiku 5.5's 100k

Watch out for

  • Prompts over 272,000 tokens are rebilled at $0.20 input and $0.75 output for the whole request
  • A twentieth of GPT-6 Sol's price reflects a capability gap, not a discount on the same model

From benchmark to production

Building on GPT-5.6 Sol or GPT-6 Luna?

Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.

FAQ

GPT-5.6 Sol vs GPT-6 Luna FAQ

Which is cheaper, GPT-5.6 Sol or GPT-6 Luna?

GPT-6 Luna is 98% cheaper per 1M input tokens than GPT-5.6 Sol, $0.10 vs $4. For 10M input and 2M output tokens, GPT-5.6 Sol costs $80 and GPT-6 Luna $2 at standard rates.

Which is cheaper with prompt caching, GPT-5.6 Sol or GPT-6 Luna?

GPT-5.6 Sol charges $0.40 per 1M cached input tokens, 90% below its standard input rate, and GPT-6 Luna $0.01, 90% below. With 9M of 10M input tokens cached and 2M output tokens, GPT-5.6 Sol costs $47.60 and GPT-6 Luna $1.19.

Which scores higher on independent benchmarks, GPT-5.6 Sol or GPT-6 Luna?

GPT-5.6 Sol leads on Arena Text, 1485 Elo to 1443 Elo; GPT-5.6 Sol leads on GPQA Diamond, 93.5% to 90.5%; GPT-5.6 Sol leads on FrontierMath Tiers 1-3 (v2), 89.1% to 78.9%. Scores are independent evaluations, not either vendor's launch figures.

Which has the larger context window, GPT-5.6 Sol or GPT-6 Luna?

Both accept 1,050,000 tokens of context. Both return up to 128,000 tokens per response.

Production evaluation

Choosing between GPT-5.6 Sol and GPT-6 Luna?

Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • A head-to-head evaluation on your real workload
  • Routing between models by cost and difficulty