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Van Data Team

text model comparison

GPT-6 Luna vs Grok 4.5

GPT-6 Luna and Grok 4.5 are both current text models, from OpenAI and xAI. GPT-6 Luna is 95% cheaper per 1M input tokens than Grok 4.5, $0.10 vs $2. On Arena Text, GPT-6 Luna scores 1443 Elo and Grok 4.5 1466 Elo (Arena, measured October 8, 2026). GPT-6 Luna has the larger context window, 1,050,000 vs 500,000 tokens.

Higher Arena Text
Grok 4.51466 Elo against 1443 Elo
Lower price per 1M input tokens
GPT-6 Luna$0.10 against $2
Longer context window
GPT-6 Luna1.05M tokens against 500K tokens
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-6 Luna and Grok 4.5, 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-6 LunaGrok 4.5
Price
Price per 1M input tokens$0.10 (better)$2
Price per 1M output tokens$0.50 (better)$6
Independent benchmarks
Arena Text1443 Elo (#37 of 49)1466 Elo (#27 of 49) (better)
GPQA Diamond90.5% (#25 of 40)93.4% (#12 of 40) (better)
FrontierMath Tiers 1-3 (v2)78.9% (#14 of 40) (better)57.2% (#30 of 40)
SimpleQA Verified41.4% (#26 of 36)48.3% (#21 of 36) (better)
Specs
ProviderOpenAIxAI
Release dateSeptember 22, 2026July 16, 2026
AccessAPI onlyAPI only
Context window1,050,000 tokens500,000 tokens
Max output128,000 tokensNot reported
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-6 Luna and Grok 4.5 land among the current models of their task, on every independent benchmark, on price and on context.

Where each stands

On Arena Text, GPT-6 Luna ranks #37 and Grok 4.5 #27 of 49 current models. Each bar is the share of current models it beats: on score, price and context.

GPT-6 Luna and Grok 4.5 among the current text models, one row per benchmark, price and context window.

Independent benchmarks

Arena Text

GPT-6 Luna:#37of 49

1,443 Elo · ahead of 23%

Grok 4.5:#27of 49

1,466 Elo · ahead of 44%

GPQA Diamond

GPT-6 Luna:#25of 40

90.5% · ahead of 36%

Grok 4.5:#12of 40

93.4% · ahead of 72%

FrontierMath Tiers 1-3 (v2)

GPT-6 Luna:#14of 40

78.9% · ahead of 67%

Grok 4.5:#30of 40

57.2% · ahead of 26%

SimpleQA Verified

GPT-6 Luna:#26of 36

41.4% · ahead of 29%

Grok 4.5:#21of 36

48.3% · ahead of 43%

Price and context

Price per 1M input tokens

GPT-6 Luna:#1of 51

$0.10 · cheaper than 96%

Grok 4.5:#28of 51

$2 · cheaper than 24%

Price per 1M output tokens

GPT-6 Luna:#3of 51

$0.50 · cheaper than 92%

Grok 4.5:#27of 51

$6 · cheaper than 42%

Context window

GPT-6 Luna:#1of 66

1.05M tokens · longer than 86%

Grok 4.5:#54of 66

500K tokens · longer than 15%

Scores fromArena · 2026-10-12Epoch AI · 2026-10-12

Quality against price

Arena Text · measured October 8, 2026

GPT-6 Luna scores 1,443 at $0.2; Grok 4.5 scores 1,466 at $3 (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-6 Luna and Grok 4.5 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)Grok 4.5GPT-6 Luna
Quality against price: the data
Arena Text score against price for 41 text models, GPT-6 Luna and Grok 4.5 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, Grok 4.5 costs 16 times as much as GPT-6 Luna, at each provider's listed rates.

What GPT-6 Luna and Grok 4.5cost for the same work, at each provider's listed rates.

  • 10M input and 2M output tokens

    Grok 4.5 costs 16× as much

    GPT-6 Luna
    $2
    Grok 4.5
    $32

  • The same, with 9M of the input tokens cached

    Grok 4.5 costs 14× as much

    GPT-6 Luna
    $1.19
    Grok 4.5
    $16.70
Workload or rateGPT-6 LunaGrok 4.5
Example workloads
10M input and 2M output tokens$2 (better)$32
The same, with 9M of the input tokens cached$1.19 (better)$16.70
Other listed rates
1M input tokens, cached$0.01 (better)$0.30
1M input tokens, prompts over 272K tokens$0.20Not listed
1M output tokens, prompts over 272K tokens$0.75Not listed

Verdict

When to choose each

GPT-6 Luna

Where it leads

  • Lower input and output prices, $0.10 and $0.50 vs $2 and $6 per 1M tokens
  • Higher FrontierMath Tiers 1-3 (v2) score, 78.9% vs 57.2%
  • Larger context window 1,050,000 vs 500,000 tokens
  • Cheaper cached input, $0.01 vs $0.30 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
Full GPT-6 Luna profile

Grok 4.5

Where it leads

  • Higher Arena Text score, 1466 Elo vs 1443 Elo
  • Higher GPQA Diamond score, 93.4% vs 90.5%
  • Higher SimpleQA Verified score, 48.3% vs 41.4%

Best for

  • Coding and agentic API evaluations below the long-context pricing threshold

Watch out for

  • The published base rates increase for requests at or above the provider long-context threshold
Full Grok 4.5 profile

From benchmark to production

Building on GPT-6 Luna or Grok 4.5?

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FAQ

GPT-6 Luna vs Grok 4.5 FAQ

Which is cheaper, GPT-6 Luna or Grok 4.5?

GPT-6 Luna is 95% cheaper per 1M input tokens than Grok 4.5, $0.10 vs $2. For 10M input and 2M output tokens, GPT-6 Luna costs $2 and Grok 4.5 $32 at standard rates.

Which is cheaper with prompt caching, GPT-6 Luna or Grok 4.5?

GPT-6 Luna charges $0.01 per 1M cached input tokens, 90% below its standard input rate, and Grok 4.5 $0.30, 85% below. With 9M of 10M input tokens cached and 2M output tokens, GPT-6 Luna costs $1.19 and Grok 4.5 $16.70.

Which scores higher on independent benchmarks, GPT-6 Luna or Grok 4.5?

Grok 4.5 leads on Arena Text, 1466 Elo to 1443 Elo; Grok 4.5 leads on GPQA Diamond, 93.4% to 90.5%; GPT-6 Luna leads on FrontierMath Tiers 1-3 (v2), 78.9% to 57.2%. Scores are independent evaluations, not either vendor's launch figures.

Which has the larger context window, GPT-6 Luna or Grok 4.5?

GPT-6 Luna has the larger context window, 1,050,000 vs 500,000 tokens.

Further reading

More on GPT-6 Luna and Grok 4.5

Articles on GPT-6 Luna and Grok 4.5

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Production evaluation

Choosing between GPT-6 Luna and Grok 4.5?

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
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