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Van Data Team

text model comparison

GLM-5.3-Flash vs Hy4

GLM-5.3-Flash and Hy4 are both current text models, from Z.ai and Tencent. GLM-5.3-Flash is 82% cheaper per 1M input tokens than Hy4, $0.15 vs $0.834.

Lower price per 1M input tokens
GLM-5.3-Flash$0.15 against $0.834
Newer release
Hy4Nov 2026 against Aug 2026

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

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Side by side

GLM-5.3-Flash and Hy4, line by line

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

FactGLM-5.3-FlashHy4
Price
Price per 1M input tokens$0.15 (better)$0.834
Price per 1M output tokens$0.50 (better)$2.501
Independent benchmarks
Arena Text1477 Elo (#20 of 52)Not measured
Arena WebDev1607 Elo (#24 of 46)Not measured
GPQA Diamond90.2% (#28 of 41)Not measured
FrontierMath Tiers 1-3 (v2)55.8% (#33 of 41)Not measured
Specs
ProviderZ.aiTencent
Release dateAugust 26, 2026November 4, 2026
AccessAPI and open weightsAPI only
Context window1,000,000 tokensNot reported
Max output128,000 tokensNot reported
Inputtext, image, videotext, 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 GLM-5.3-Flash and Hy4 land among the current models of their task, on every independent benchmark, on price and on context.

Where each stands

Only GLM-5.3-Flash has a current Arena Text score: #20 of 52. Each bar is the share of current models it beats.

GLM-5.3-Flash and Hy4 among the current text models, one row per benchmark, price and context window.

Independent benchmarks

Arena Text

GLM-5.3-Flash:#20of 52

1,477 Elo · ahead of 61%

Hy4: Not scored

Arena WebDev

GLM-5.3-Flash:#24of 46

1,607 Elo · ahead of 49%

Hy4: Not scored

GPQA Diamond

GLM-5.3-Flash:#28of 41

90.2% · ahead of 30%

Hy4: Not scored

FrontierMath Tiers 1-3 (v2)

GLM-5.3-Flash:#33of 41

55.8% · ahead of 20%

Hy4: Not scored

Price and context

Price per 1M input tokens

GLM-5.3-Flash:#5of 55

$0.15 · cheaper than 91%

Hy4:#21of 55

$0.834 · cheaper than 63%

Price per 1M output tokens

GLM-5.3-Flash:#3of 55

$0.50 · cheaper than 93%

Hy4:#20of 55

$2.501 · cheaper than 65%

Context window

GLM-5.3-Flash:#26of 64

1M tokens · longer than 22%

Hy4: Not listed

Scores fromArena · 2026-11-03Epoch AI · 2026-11-03

Quality against price

Arena Text · measured October 28, 2026

GLM-5.3-Flash scores 1,477 at $0.238 (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, GLM-5.3-Flash and Hy4 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)GLM-5.3-Flash
Quality against price: the data
Arena Text score against price for 44 text models, GLM-5.3-Flash and Hy4 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, Hy4 costs 5.3 times as much as GLM-5.3-Flash, at each provider's listed rates.

What GLM-5.3-Flash and Hy4 cost for the same work, at the rates each provider lists.

  • 10M input and 2M output tokens

    Hy4 costs 5.3× as much

    GLM-5.3-Flash
    $2.50
    Hy4
    $13.34

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

    Hy4 costs 4.4× as much

    GLM-5.3-Flash
    $1.42
    Hy4
    $6.21
Workload or rateGLM-5.3-FlashHy4
Example workloads
10M input and 2M output tokens$2.50 (better)$13.34
The same, with 9M of the input tokens cached$1.42 (better)$6.21
Other listed rates
1M input tokens, cached$0.03 (better)$0.042

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

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Verdict

When to choose each

GLM-5.3-Flash

Where it leads

  • Lower input and output prices, $0.15 and $0.50 vs $0.834 and $2.501 per 1M tokens
  • Accepts video input
  • Open weights you can run yourself
  • Cheaper cached input, $0.03 vs $0.042 per 1M tokens

Best for

  • Low-cost coding from screenshots or screen recordings
  • Document and research agents on a budget

Watch out for

  • Thinking cannot be disabled
  • A faster GLM-5.3-FlashX is a separate model id at $0.37 input and $1.25 output per 1M tokens
Full GLM-5.3-Flash profile

Hy4

Where it leads

No measured lead over GLM-5.3-Flash in this data.

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
Full Hy4 profile

From benchmark to production

Building on GLM-5.3-Flash or Hy4?

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

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FAQ

GLM-5.3-Flash vs Hy4 FAQ

Which is cheaper, GLM-5.3-Flash or Hy4?

GLM-5.3-Flash is 82% cheaper per 1M input tokens than Hy4, $0.15 vs $0.834. For 10M input and 2M output tokens, GLM-5.3-Flash costs $2.50 and Hy4 $13.34 at standard rates.

Which is cheaper with prompt caching, GLM-5.3-Flash or Hy4?

GLM-5.3-Flash charges $0.03 per 1M cached input tokens, 80% below its standard input rate, and Hy4 $0.042, 95% below. With 9M of 10M input tokens cached and 2M output tokens, GLM-5.3-Flash costs $1.42 and Hy4 $6.21.

How do GLM-5.3-Flash and Hy4 differ in features?

GLM-5.3-Flash takes text, image and video input; Hy4 takes text and image.

Can I run GLM-5.3-Flash or Hy4 myself?

GLM-5.3-Flash is released as open weights you can run yourself; Hy4 is available through its provider's API only.

Further reading

More on GLM-5.3-Flash and Hy4

Related comparisons

Production evaluation

Choosing between GLM-5.3-Flash and Hy4?

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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