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text model comparison

Gemini 4 Carbon vs Kimi K3.1 Pro

Gemini 4 Carbon and Kimi K3.1 Pro are both current text models, from Google and Moonshot Kimi. Both cost $2 per 1M input tokens. On Arena WebDev, Gemini 4 Carbon scores 1768 Elo and Kimi K3.1 Pro 1688 Elo (Arena, measured November 10, 2026). Both accept 1,048,576 tokens of context.

Higher Arena WebDev
Gemini 4 Carbon1768 Elo against 1688 Elo
Newer release
Gemini 4 CarbonOct 28, 2026 against Oct 23, 2026

Data verified November 11, 2026 · Dataset 2026-11-11

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Side by side

Gemini 4 Carbon and Kimi K3.1 Pro, line by line

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

FactGemini 4 CarbonKimi K3.1 Pro
Price
Price per 1M input tokens$2$2
Price per 1M output tokens$10$10
Independent benchmarks
Arena WebDev1768 Elo (#4 of 44) (better)1688 Elo (#7 of 44)
GPQA Diamond96.5% (#1 of 40) (better)94.2% (#10 of 40)
FrontierMath Tiers 1-3 (v2)90.5% (#6 of 39) (better)78.2% (#17 of 39)
SimpleQA Verified77.7% (#2 of 36) (better)62.2% (#13 of 36)
Arena Text1526 Elo (#1 of 48)Not measured
Specs
ProviderGoogleMoonshot Kimi
Release dateOctober 28, 2026October 23, 2026
AccessAPI onlyAPI and open weights
Context window1,048,576 tokens1,048,576 tokens
Max output65,536 tokens1,048,576 tokens
Inputtext, image, audio, videotext, image, video
Outputtexttext

Scores:Arena · 2026-11-11Epoch AI · 2026-11-11

Open both in the interactive comparison

Charts

Against every current text model

Where Gemini 4 Carbon and Kimi K3.1 Pro land among the current models of their task, on every independent benchmark, on price and on context.

Where each stands

On Arena WebDev, Gemini 4 Carbon ranks #4 and Kimi K3.1 Pro #7 of 44 current models. Each bar is the share of current models it beats: on score, price and context.

Gemini 4 Carbon and Kimi K3.1 Pro among the current text models, one row per benchmark, price and context window.

Independent benchmarks

Arena Text

Gemini 4 Carbon:#1of 48

1,526 Elo · ahead of 100%

Kimi K3.1 Pro: Not scored

Arena WebDev

Gemini 4 Carbon:#4of 44

1,768 Elo · ahead of 93%

Kimi K3.1 Pro:#7of 44

1,688 Elo · ahead of 86%

GPQA Diamond

Gemini 4 Carbon:#1of 40

96.5% · ahead of 100%

Kimi K3.1 Pro:#10of 40

94.2% · ahead of 77%

FrontierMath Tiers 1-3 (v2)

Gemini 4 Carbon:#6of 39

90.5% · ahead of 87%

Kimi K3.1 Pro:#17of 39

78.2% · ahead of 58%

SimpleQA Verified

Gemini 4 Carbon:#2of 36

77.7% · ahead of 97%

Kimi K3.1 Pro:#13of 36

62.2% · ahead of 66%

Price and context

Price per 1M input tokens

Gemini 4 Carbon:#33of 57

$2 · cheaper than 23%

Kimi K3.1 Pro:#33of 57

$2 · cheaper than 23%

Price per 1M output tokens

Gemini 4 Carbon:#38of 57

$10 · cheaper than 25%

Kimi K3.1 Pro:#38of 57

$10 · cheaper than 25%

Context window

Gemini 4 Carbon:#12of 67

1.05M tokens · longer than 64%

Kimi K3.1 Pro:#12of 67

1.05M tokens · longer than 64%

Quality against price

Arena Text · measured November 9, 2026

Gemini 4 Carbon scores 1,526 at $4 (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 40 text models, Gemini 4 Carbon and Kimi K3.1 Pro marked.1,2501,3001,3501,4001,4501,5001,550$0.30$1$3$10MOST ATTRACTIVE QUADRANTMedian price $2.08Median 1,472Blended price per 1M tokens (3 input : 1 output), log scaleArena Text (Elo)Gemini 4 Carbon
Quality against price: the data
Arena Text score against price for 40 text models, Gemini 4 Carbon and Kimi K3.1 Pro marked.
ModelProviderBlended price per 1M tokensArena Text (Elo)
Gemini 4 CarbonGoogle$41,526
Gemini 4 ArgonGoogle$41,522
Muse WonderMeta$81,511
Claude Opus 5.5Anthropic$81,506
Gemini 3.8 FlashGoogle$1.501,497
Muse Spark 1.3Meta$21,496
Kimi K3Moonshot Kimi$61,487
GPT-5.6 SolOpenAI$81,485
Qwen3.8-MaxAlibaba Qwen$31,484
Gemini 3.6 FlashGoogle$31,483
GLM-5.3Z.ai$2.151,483
GPT-6.1 SolOpenAI$41,482
GPT-5.5OpenAI$111,481
MiMo-V2.6-ProXiaomi MiMo$0.541,480
GPT-5.4OpenAI$5.631,475
DeepSeek-V4.1-FlashDeepSeek$0.521,475
GPT-6 AstraOpenAI$201,474
GLM-5.3-FlashZ.ai$0.241,474
Gemini 3 Flash PreviewGoogle$1.131,473
Grok 4.20xAI$1.561,472
Claude Sonnet 5.5Anthropic$41,472
GPT-5.6 TerraOpenAI$4.501,468
Grok 4.5xAI$31,466
DeepSeek-V4-ProDeepSeek$1.981,464
GPT-6 SolOpenAI$41,457
Grok 4.6xAI$31,456
Gemini 3.5 Flash-LiteGoogle$0.851,455
GPT-5.6 LunaOpenAI$0.451,453
MiMo-V2.6-FlashXiaomi MiMo$0.181,448
Step 5 PreviewStepFun$1.431,448
Grok 4.3xAI$1.561,442
Grok 4.7xAI$31,442
GPT-6 LunaOpenAI$0.201,441
Mistral Large 4Mistral AI$1.051,423
Claude Haiku 5.5Anthropic$0.201,422
GPT-4.1OpenAI$3.501,415
Inkling 2 SmallThinking Machines Lab$0.471,412
Solar Pro 4Upstage$0.521,387
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

At each provider's listed rates.

What Gemini 4 Carbon and Kimi K3.1 Pro cost for the same work, at the rates each provider lists.
  • 10M input and 2M output tokens

    The same price

    Gemini 4 Carbon
    $40
    Kimi K3.1 Pro
    $40
  • The same, with 9M of the input tokens cached

    Kimi K3.1 Pro costs 1.0× as much

    Gemini 4 Carbon
    $22.90
    Kimi K3.1 Pro
    $23.80
Workload or rateGemini 4 CarbonKimi K3.1 Pro
Example workloads
10M input and 2M output tokens$40$40
The same, with 9M of the input tokens cached$22.90 (better)$23.80
The same, through the batch API$20Not listed
Other listed rates
1M input tokens, cached$0.10 (better)$0.20
1M input tokens, 5-minute cache writeNot listed$2
1M input tokens, 1-hour cache writeNot listed$4
1M input tokens, batch$1Not listed
1M output tokens, batch$5Not listed
1M input tokens, Highspeed endpointNot listed$4
1M output tokens, Highspeed endpointNot listed$20

Your own bill will differ. These figures use list prices and example workloads. The AI Model Cost Audit reviews your real usage, shortlists models that score as well for each task for less, and estimates the savings.

Launch price $100, regular price $150

Book the cost audit

Verdict

When to choose each

Gemini 4 Carbon

Where it leads

  • Higher Arena WebDev score, 1768 Elo vs 1688 Elo
  • Higher GPQA Diamond score, 96.5% vs 94.2%
  • Higher FrontierMath Tiers 1-3 (v2) score, 90.5% vs 78.2%
  • Higher SimpleQA Verified score, 77.7% vs 62.2%
  • Accepts audio input
  • Cheaper cached input, $0.10 vs $0.20 per 1M tokens
  • Batch pricing, $1 input and $5 output per 1M tokens

Best for

  • Finding, validating and patching vulnerabilities
  • Long-horizon engineering and research over very long inputs

Watch out for

  • Generally available in the Gemini API since November 10, 2026, on the paid tier only (there is no free tier), after two weeks open only to cyber defenders in its Fairwind Program
  • The $2 and $10 rates are introductory and end on November 30, 2026: from December 1, input, output and cached input double, to $4, $20 and $0.20 per 1M tokens (Batch from $1 and $5 to $2 and $10)
  • In Google's own table it trails GPT-6 Astra on FrontierSWE 2 (61.2% against 65.5%) and Claude Opus 5.5 on OSWorld-2.1 (81.5% against 87.2%)
  • A request returns at most 65,536 output tokens by default; longer outputs, up to 1,048,576 tokens, need Google long-decode mode

Kimi K3.1 Pro

Where it leads

  • Longer responses, up to 1,048,576 vs 65,536 output tokens
  • Open weights you can run yourself

Best for

  • Long-horizon coding and agent work at mid-tier prices
  • Long documents, images and video in one 1M-token context

Watch out for

  • Thinking is always on: reasoning effort runs low, high or max, and max is the default
  • The Highspeed endpoint, two to three times faster by Moonshot's account, costs twice as much at $4 input and $20 output per 1M tokens
  • The weights carry Moonshot's own license: a model-as-a-service business above $20M in yearly revenue needs a separate agreement, and products above 100M monthly users or $20M monthly revenue must display the Kimi K3.1 Pro name
  • Self-hosting takes a multi-node GPU cluster: the MXFP4 weights alone are about 1.56 TB
  • Arena Text has not rated it yet

From benchmark to production

Building on Gemini 4 Carbon or Kimi K3.1 Pro?

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.

AI Model Cost AuditLaunch price $100, regular price $150What the audit includes

FAQ

Gemini 4 Carbon vs Kimi K3.1 Pro FAQ

Which is cheaper, Gemini 4 Carbon or Kimi K3.1 Pro?

Both cost $2 per 1M input tokens. For 10M input and 2M output tokens, Gemini 4 Carbon costs $40 and Kimi K3.1 Pro $40 at standard rates.

Which is cheaper with prompt caching, Gemini 4 Carbon or Kimi K3.1 Pro?

Gemini 4 Carbon charges $0.10 per 1M cached input tokens, 95% below its standard input rate, and Kimi K3.1 Pro $0.20, 90% below. With 9M of 10M input tokens cached and 2M output tokens, Gemini 4 Carbon costs $22.90 and Kimi K3.1 Pro $23.80.

Which scores higher on independent benchmarks, Gemini 4 Carbon or Kimi K3.1 Pro?

Gemini 4 Carbon leads on Arena WebDev, 1768 Elo to 1688 Elo; Gemini 4 Carbon leads on GPQA Diamond, 96.5% to 94.2%; Gemini 4 Carbon leads on FrontierMath Tiers 1-3 (v2), 90.5% to 78.2%. Scores are independent evaluations, not either vendor's launch figures.

Which has the larger context window, Gemini 4 Carbon or Kimi K3.1 Pro?

Both accept 1,048,576 tokens of context. Kimi K3.1 Pro returns up to 1,048,576 tokens per response, Gemini 4 Carbon 65,536.

How do Gemini 4 Carbon and Kimi K3.1 Pro differ in features?

Gemini 4 Carbon takes text, image, audio and video input; Kimi K3.1 Pro takes text, image and video.

Can I run Gemini 4 Carbon or Kimi K3.1 Pro myself?

Kimi K3.1 Pro is released as open weights you can run yourself; Gemini 4 Carbon is available through its provider's API only.

Production evaluation

Choosing between Gemini 4 Carbon and Kimi K3.1 Pro?

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