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Model Comparison

Qwen3.8 Omni Flash vs GLM 5.3 FlashWhich Is Better in 2026?

Qwen: Qwen3.8 Omni Flash logoQwen•
Z.AI

Qwen3.8 Omni Flash vs GLM 5.3 Flash: which should you choose in 2026?

Qwen3.8 Omni Flash (by Qwen) and GLM 5.3 Flash (by Z.AI) 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.

Their blended API prices are effectively the same.

Choose Qwen3.8 Omni Flash if…

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

Choose GLM 5.3 Flash if…

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

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.

Chat with both models
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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.

Qwen3.8 Omni Flash and GLM 5.3 Flash benchmark results
BenchmarkQwen3.8 Omni FlashGLM 5.3 Flash
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

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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

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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

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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

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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

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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

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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

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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

——
Arena preference scores
Arena (Text)

Human preference score

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1473 ±5
Arena (Code/WebDev)

Human preference score for code and web development

—
1616 ±8

Benchmark sources

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

Pricing, capabilities, and model facts

Qwen3.8 Omni Flash and GLM 5.3 Flash model facts
FeatureQwen3.8 Omni FlashGLM 5.3 Flash
Context & model facts
DeveloperQwenZ.AI
API providerQwenZ.ai
Input context1,000,000 tokens1,048,576 tokens
Maximum output131,072 tokens943,717 tokens
Released——
Added to WritingmateSep 21, 2026Aug 26, 2026
LicenseNot availableNot available
Knowledge cutoff——
Capabilities
InputsText, Image, Audio, VideoText, Image, Video
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.15$0.15
Output (per 1M tokens)$0.47$0.50
Blended 3:1 input/output$0.23$0.2375
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 Sep 21, 2026.

Why Pay for Multiple Subscriptions?

Comparing Qwen3.8 Omni Flash from Qwen with GLM 5.3 Flash from Z.AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Qwen3.8 Omni Flash and GLM 5.3 Flash
PlanPriceQwen3.8 Omni FlashGLM 5.3 FlashAI 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

Qwen3.8 Omni Flash vs GLM 5.3 Flash FAQ

Which is better, Qwen3.8 Omni Flash or GLM 5.3 Flash?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Their blended API prices are effectively the same.

Which model is cheaper to use through an API?

Their blended API prices are effectively the same.

Which model supports more context?

The published context windows are close enough that this page does not treat the difference as a material advantage.

Can I switch between Qwen3.8 Omni Flash and GLM 5.3 Flash?

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.