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text model comparison

Hy4 vs Kimi K3

Hy4 and Kimi K3 are both current text models, from Tencent and Moonshot Kimi. Hy4 is 72% cheaper per 1M input tokens than Kimi K3, $0.834 vs $3.

Lower price per 1M input tokens
Hy4$0.834 against $3
Newer release
Hy4Nov 2026 against Jul 2026

Data verified November 5, 2026 · Dataset 2026-11-05

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Side by side

Hy4 and Kimi K3, line by line

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

FactHy4Kimi K3
Price
Price per 1M input tokens$0.834 (better)$3
Price per 1M output tokens$2.501 (better)$15
Independent benchmarks
Arena TextNot measured1488 Elo (#10 of 52)
Arena WebDevNot measured1656 Elo (#14 of 46)
GPQA DiamondNot measured93.1% (#17 of 41)
FrontierMath Tiers 1-3 (v2)Not measured72.2% (#22 of 41)
SimpleQA VerifiedNot measured50.6% (#20 of 38)
Specs
ProviderTencentMoonshot Kimi
Release dateNovember 4, 2026July 16, 2026
AccessAPI onlyAPI and open weights
Context windowNot reported1,048,576 tokens
Max outputNot reportedNot reported
Inputtext, imagetext, image
Outputtexttext

No independent benchmark has scored both models yet. Each rank counts the current models scored on that benchmark.

Scores:Arena · 2026-11-03Epoch AI · 2026-11-03

Charts

Against every current text model

Where Hy4 and Kimi K3 land among the current models of their task, on every independent benchmark, on price and on context.

Where each stands

Only Kimi K3 has a current Arena Text score: #10 of 52. Each bar is the share of current models it beats.

Hy4 and Kimi K3 among the current text models, one row per benchmark, price and context window.

Independent benchmarks

Arena Text

Hy4: Not scored

Kimi K3:#10of 52

1,488 Elo · ahead of 82%

Arena WebDev

Hy4: Not scored

Kimi K3:#14of 46

1,656 Elo · ahead of 69%

GPQA Diamond

Hy4: Not scored

Kimi K3:#17of 41

93.1% · ahead of 60%

FrontierMath Tiers 1-3 (v2)

Hy4: Not scored

Kimi K3:#22of 41

72.2% · ahead of 48%

SimpleQA Verified

Hy4: Not scored

Kimi K3:#20of 38

50.6% · ahead of 49%

Price and context

Price per 1M input tokens

Hy4:#21of 55

$0.834 · cheaper than 63%

Kimi K3:#44of 55

$3 · cheaper than 20%

Price per 1M output tokens

Hy4:#20of 55

$2.501 · cheaper than 65%

Kimi K3:#43of 55

$15 · cheaper than 20%

Context window

Hy4: Not listed

Kimi K3:#11of 64

1.05M tokens · longer than 67%

Quality against price

Arena Text · measured October 28, 2026

Kimi K3 scores 1,488 at $6 (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 44 text models, Hy4 and Kimi K3 marked.1,2501,3001,3501,4001,4501,5001,550$0.30$1$3$10MOST ATTRACTIVE QUADRANTMedian price $3Median 1,476Blended price per 1M tokens (3 input : 1 output), log scaleArena Text (Elo)Kimi K3
Quality against price: the data
Arena Text score against price for 44 text models, Hy4 and Kimi K3 marked.
ModelProviderBlended price per 1M tokensArena Text (Elo)
Gemini 4 CarbonGoogle$41,528
Gemini 4 ArgonGoogle$41,520
Claude Opus 5.5Anthropic$81,509
Muse WonderMeta$81,507
Claude Fable 5Anthropic$201,504
Claude Fable 5.1Anthropic$201,504
Gemini 3.8 FlashGoogle$1.501,496
Muse Spark 1.3Meta$21,495
Claude Opus 5Anthropic$101,492
Kimi K3Moonshot Kimi$61,488
GPT-5.6 SolOpenAI$81,485
Gemini 3.6 FlashGoogle$31,483
GPT-5.5OpenAI$111,482
Claude Opus 4.8Anthropic$101,482
Qwen3.8-MaxAlibaba Qwen$31,482
GPT-6.1 SolOpenAI$41,481
GLM-5.3Z.ai$2.151,481
MiMo-V2.6-ProXiaomi MiMo$0.541,480
Claude Sonnet 5.5Anthropic$41,477
GLM-5.3-FlashZ.ai$0.241,477
GPT-6 AstraOpenAI$201,476
DeepSeek-V4.1-FlashDeepSeek$0.521,476
GPT-5.4OpenAI$5.631,475
Gemini 3 Flash PreviewGoogle$1.131,473
Grok 4.20xAI$1.561,472
GPT-5.6 TerraOpenAI$4.501,467
Grok 4.5xAI$31,465
DeepSeek-V4-ProDeepSeek$1.981,465
Claude Sonnet 5Anthropic$41,462
GPT-6 SolOpenAI$41,458
Gemini 3.5 Flash-LiteGoogle$0.851,456
Grok 4.6xAI$31,455
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
GPT-6 LunaOpenAI$0.201,441
Grok 4.7xAI$31,441
Mistral Large 4Mistral AI$1.051,432
Claude Haiku 5.5Anthropic$0.201,425
GPT-4.1OpenAI$3.501,415
Solar Pro 4Upstage$0.521,388
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, Kimi K3 costs 4.5 times as much as Hy4, at each provider's listed rates.

What Hy4 and Kimi K3 cost for the same work, at the rates each provider lists.
  • 10M input and 2M output tokens

    Kimi K3 costs 4.5× as much

    Hy4
    $13.34
    Kimi K3
    $60
  • The same, with 9M of the input tokens cached

    Kimi K3 costs 5.7× as much

    Hy4
    $6.21
    Kimi K3
    $35.70
Workload or rateHy4Kimi K3
Example workloads
10M input and 2M output tokens$13.34 (better)$60
The same, with 9M of the input tokens cached$6.21 (better)$35.70
Other listed rates
1M input tokens, cached$0.042 (better)$0.30

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

Hy4

Where it leads

  • Lower input and output prices, $0.834 and $2.501 vs $3 and $15 per 1M tokens
  • Cheaper cached input, $0.042 vs $0.30 per 1M tokens

Best for

  • Agents that work across terminals, desktop apps and browsers on a tight budget
  • Finance and office work: research reports, spreadsheets and slides

Watch out for

  • API only for now: Tencent says it will publish the weights and a system card later, and only Hy4 preview (August 2026, Apache 2.0) is open today
  • In Tencent's own table it scores 44.4% on Terminal-Bench 4.0 against 66.8% for Kimi K3.1 Pro, and Tencent says a clear gap remains to top proprietary models on research-level math, long-horizon coding and hard visual questions
  • Tencent publishes no context window or output limit for Hy4 (the preview card lists 1M tokens), and no independent benchmark score is recorded here yet

Kimi K3

Where it leads

  • Open weights you can run yourself

Best for

  • Teams that want a frontier-scale model they can also self-host, with one architecture across both paths
  • Long-context work at mid-tier hosted pricing

Watch out for

  • Hosted rates match Claude Sonnet 5 exactly at $3 / $15 while offering a larger context window, so the comparison turns on capability rather than price
  • Running the open weights shifts cost to infrastructure, which the token rates recorded here do not represent
  • Moonshot’s self-reported results place it ahead of Claude Opus 4.8 and GPT-5.5 but behind Claude Fable 5 and GPT-5.6 Sol; none of this was reproduced independently here

From benchmark to production

Building on Hy4 or Kimi K3?

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

Hy4 vs Kimi K3 FAQ

Which is cheaper, Hy4 or Kimi K3?

Hy4 is 72% cheaper per 1M input tokens than Kimi K3, $0.834 vs $3. For 10M input and 2M output tokens, Hy4 costs $13.34 and Kimi K3 $60 at standard rates.

Which is cheaper with prompt caching, Hy4 or Kimi K3?

Hy4 charges $0.042 per 1M cached input tokens, 95% below its standard input rate, and Kimi K3 $0.30, 90% below. With 9M of 10M input tokens cached and 2M output tokens, Hy4 costs $6.21 and Kimi K3 $35.70.

Can I run Hy4 or Kimi K3 myself?

Kimi K3 is released as open weights you can run yourself; Hy4 is available through its provider's API only.

Production evaluation

Choosing between Hy4 and Kimi K3?

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