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Model Comparison

GPT-5.2-Codex vs Hy4Which Is Better in 2026?

OpenAI: GPT-5.2-Codex logoOpenAI•Tencent: Hy4 logoTencent

GPT-5.2-Codex vs Hy4: which should you choose in 2026?

GPT-5.2-Codex (by OpenAI) and Hy4 (by Tencent) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

Hy4 is about 7.7× cheaper on a blended 3:1 input/output basis ($0.625375 vs $4.81 per 1M tokens).

Hy4 has the larger context window (1,048,576 tokens vs 400,000 tokens).

Choose GPT-5.2-Codex if…

  • • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage

Choose Hy4 if…

  • • lower blended API cost matters for your workload
  • • you work with longer documents, transcripts, or codebases

The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.

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Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

GPT-5.2-Codex and Hy4 benchmark results
BenchmarkGPT-5.2-CodexHy4
AutomationBench-AA

A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework.

Guardrail-aware score

——
BrowseComp

A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing.

Accuracy

——
OSWorld-Verified

A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state.

Mean task reward

——
SWE-Bench Verified

A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests.

Resolved

72.8%
SWE-bench
—
Terminal-Bench 2.1

Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol.

Mean task success

——
AutoBench

A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench.

Weighted peer-assessment score

——
GPQA Diamond

The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry.

Accuracy

——
Humanity's Last Exam

A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged.

Accuracy

——
LiveBench

A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol.

Mean of category averages

74.0%
LiveBench
—
Arena preference scores
Arena (Code/WebDev)

Human preference score for code and web development

1339 ±9
—

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Oct 5, 2026.

Pricing, capabilities, and model facts

GPT-5.2-Codex and Hy4 model facts
FeatureGPT-5.2-CodexHy4
Context & model facts
DeveloperOpenAITencent
API providerOpenAITencent
Input context400,000 tokens1,048,576 tokens
Maximum output128,000 tokens65,536 tokens
Released——
Added to WritingmateJan 14, 2026Nov 4, 2026
LicenseNot availableNot available
Knowledge cutoff——
Capabilities
InputsText, ImageText, Image
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$1.75$0.417
Output (per 1M tokens)$14.00$1.25
Blended 3:1 input/output$4.81$0.625375
API performance
p95 latencyNot measuredNot measured
Output throughputNot measuredNot measured
Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table.

Data sources

  • Writingmate model catalog (pricing, limits, and availability)

Catalog data last updated Nov 4, 2026.

Why Pay for Multiple Subscriptions?

Comparing GPT-5.2-Codex from OpenAI with Hy4 from Tencent? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for GPT-5.2-Codex and Hy4
PlanPriceGPT-5.2-CodexHy4AI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0
View Plans & Pricing

GPT-5.2-Codex vs Hy4 FAQ

Which is better, GPT-5.2-Codex or Hy4?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Hy4 is about 7.7× cheaper on a blended 3:1 input/output basis ($0.625375 vs $4.81 per 1M tokens). Hy4 has the larger context window (1,048,576 tokens vs 400,000 tokens).

Which model is cheaper to use through an API?

Hy4 is about 7.7× cheaper on a blended 3:1 input/output basis ($0.625375 vs $4.81 per 1M tokens).

Which model supports more context?

Hy4 has the larger context window (1,048,576 tokens vs 400,000 tokens).

Can I switch between GPT-5.2-Codex and Hy4?

Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.