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text model comparison

Claude Opus 5.5 vs Hy4

Claude Opus 5.5 and Hy4 are both current text models, from Anthropic and Tencent. Hy4 is 79% cheaper per 1M input tokens than Claude Opus 5.5, $0.834 vs $4. In Tencent's Hy4 launch table, Claude Opus 5.5 scores 72.6% on DeepSWE v1.1 and Hy4 74.9%.

Lower price per 1M input tokens
Hy4$0.834 against $4
Newer release
Hy4Nov 2026 against Sep 2026

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

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Side by side

Claude Opus 5.5 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.

FactClaude Opus 5.5Hy4
Price
Price per 1M input tokens$4$0.834 (better)
Price per 1M output tokens$20$2.501 (better)
Independent benchmarks
Arena Text1509 Elo (#3 of 52)Not measured
Arena WebDev1810 Elo (#1 of 46)Not measured
GPQA Diamond90.6% (#25 of 41)Not measured
FrontierMath Tiers 1-3 (v2)91.2% (#3 of 41)Not measured
SimpleQA Verified72.2% (#5 of 38)Not measured
Specs
ProviderAnthropicTencent
Release dateSeptember 22, 2026November 4, 2026
AccessAPI onlyAPI only
Context window1,000,000 tokensNot reported
Max output128,000 tokensNot 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

Provider results

What the labs report

Results tables a lab published with one of its models, where it lists both Claude Opus 5.5 and Hy4. These are the lab's own figures, not independent measurements, and each table is read on its own: labs run benchmarks with different harnesses and budgets.

Tencent, Hy4 launch table, November 4, 2026Tencent · 2026-11-05

BenchmarkClaude Opus 5.5Hy4
DeepSWE v1.172.6%74.9% (better)
SWE-Marathon v1.144.4% (better)43.8%
ProgramBenchalmost solved56.5% (better)30.0%
Terminal-Bench 4.066.4% (better)44.4%
FrontierCode 2.046.4% (better)31.0%
APEX-Agents v1.1pass@174.8%75.2% (better)
Agents' Last Exam v1.1pass rate43.4% (better)41.0%
AutomationBench v1.0.658.8% (better)56.2%
HLE-Fullwith tools66.2% (better)59.1%
Terminal-Bench-Science 0.156.2% (better)19.8%
PostTrainBench v1.244.2% (better)35.2%
OSWorld 2.1full, partial score81.8% (better)78.9%
MMMU-Pro88.2% (better)78.6%
ZeroBench mainwith tools, pass@563.5% (better)55.4%

Tencent's appendix to the Hy4 announcement, every model at its highest reasoning setting. Where Tencent lists a lab's reported score beside its own run, the score Tencent measured is shown. DeepSeek-V4.1-Flash is the 1017 build. Left out here: Tencent's internal benchmarks, the Hy4 preview column and GLM-5.3 on HLE, which Tencent ran on the text-only subset.

Charts

Against every current text model

Where Claude Opus 5.5 and Hy4 land among the current models of their task, on every independent benchmark, on price and on context.

Where each stands

Only Claude Opus 5.5 has a current Arena Text score: #3 of 52. Each bar is the share of current models it beats.

Claude Opus 5.5 and Hy4 among the current text models, one row per benchmark, price and context window.

Independent benchmarks

Arena Text

Claude Opus 5.5:#3of 52

1,509 Elo · ahead of 96%

Hy4: Not scored

Arena WebDev

Claude Opus 5.5:#1of 46

1,810 Elo · ahead of 100%

Hy4: Not scored

GPQA Diamond

Claude Opus 5.5:#25of 41

90.6% · ahead of 40%

Hy4: Not scored

FrontierMath Tiers 1-3 (v2)

Claude Opus 5.5:#3of 41

91.2% · ahead of 95%

Hy4: Not scored

SimpleQA Verified

Claude Opus 5.5:#5of 38

72.2% · ahead of 89%

Hy4: Not scored

Price and context

Price per 1M input tokens

Claude Opus 5.5:#45of 55

$4 · cheaper than 15%

Hy4:#21of 55

$0.834 · cheaper than 63%

Price per 1M output tokens

Claude Opus 5.5:#45of 55

$20 · cheaper than 15%

Hy4:#20of 55

$2.501 · cheaper than 65%

Context window

Claude Opus 5.5:#26of 64

1M tokens · longer than 22%

Hy4: Not listed

Quality against price

Arena Text · measured October 28, 2026

Claude Opus 5.5 scores 1,509 at $8 (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, Claude Opus 5.5 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)Claude Opus 5.5
Quality against price: the data
Arena Text score against price for 44 text models, Claude Opus 5.5 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, Claude Opus 5.5 costs 6.0 times as much as Hy4, at each provider's listed rates.

What Claude Opus 5.5 and Hy4 cost for the same work, at the rates each provider lists.
  • 10M input and 2M output tokens

    Claude Opus 5.5 costs 6.0× as much

    Claude Opus 5.5
    $80
    Hy4
    $13.34
  • The same, with 9M of the input tokens cached

    Claude Opus 5.5 costs 7.4× as much

    Claude Opus 5.5
    $45.80
    Hy4
    $6.21
Workload or rateClaude Opus 5.5Hy4
Example workloads
10M input and 2M output tokens$80$13.34 (better)
The same, with 9M of the input tokens cached$45.80$6.21 (better)
The same, through the batch API$40Not listed
Other listed rates
1M input tokens, cached$0.20$0.042 (better)
1M input tokens, 5-minute cache write$5Not listed
1M input tokens, 1-hour cache write$8Not listed
1M input tokens, batch$2Not listed
1M output tokens, batch$10Not listed

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

Claude Opus 5.5

Where it leads

  • Batch pricing, $2 input and $10 output per 1M tokens

Best for

  • Long-running agentic coding and knowledge work as the general-purpose default
  • Cache-heavy agent loops where the 0.05x read multiplier compounds

Watch out for

  • The API effort default is medium here, not high as on Claude Fable 5.1 and Claude Sonnet 5.5, so a like-for-like comparison has to set effort explicitly
  • The 4.7-generation tokenizer produces roughly 30% more tokens for the same text than Claude Sonnet 4.6 and earlier, so per-token rates do not compare directly across generations

Hy4

Where it leads

  • Lower input and output prices, $0.834 and $2.501 vs $4 and $20 per 1M tokens
  • Cheaper cached input, $0.042 vs $0.20 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

From benchmark to production

Building on Claude Opus 5.5 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

FAQ

Claude Opus 5.5 vs Hy4 FAQ

Which is cheaper, Claude Opus 5.5 or Hy4?

Hy4 is 79% cheaper per 1M input tokens than Claude Opus 5.5, $0.834 vs $4. For 10M input and 2M output tokens, Claude Opus 5.5 costs $80 and Hy4 $13.34 at standard rates.

Which is cheaper with prompt caching, Claude Opus 5.5 or Hy4?

Claude Opus 5.5 charges $0.20 per 1M cached input tokens, 95% below its standard input rate, and Hy4 $0.042, 95% below. With 9M of 10M input tokens cached and 2M output tokens, Claude Opus 5.5 costs $45.80 and Hy4 $6.21.

How do Claude Opus 5.5 and Hy4 compare in Tencent's own results?

In Hy4 launch table (November 4, 2026), Hy4 leads on DeepSWE v1.1, 74.9% to 72.6%; Claude Opus 5.5 leads on SWE-Marathon v1.1, 44.4% to 43.8%; Claude Opus 5.5 leads on ProgramBench (almost solved), 56.5% to 30.0%. These are Tencent's figures, not independent measurements.

Production evaluation

Choosing between Claude Opus 5.5 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