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Model Comparison

MiniMax M2 vs GLM 5.3Which Is Better in 2026?

MiniMax: MiniMax M2 logoMiniMax•
Z.AI

MiniMax M2 vs GLM 5.3: which should you choose in 2026?

MiniMax M2 (by MiniMax) and GLM 5.3 (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.

MiniMax M2 is about 4.8× cheaper on a blended 3:1 input/output basis ($0.44625 vs $2.15 per 1M tokens).

GLM 5.3 has the larger context window (1,048,576 tokens vs 204,800 tokens).

Choose MiniMax M2 if…

  • • lower blended API cost matters for your workload

Choose GLM 5.3 if…

  • • 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.

MiniMax M2 and GLM 5.3 benchmark results
BenchmarkMiniMax M2GLM 5.3
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

61.0%
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

——
Arena preference scores
Arena (Text)

Human preference score

1346 ±8
—
Arena (Code/WebDev)

Human preference score for code and web development

1297 ±11
—

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 23, 2026.

Pricing, capabilities, and model facts

MiniMax M2 and GLM 5.3 model facts
FeatureMiniMax M2GLM 5.3
Context & model facts
DeveloperMiniMaxZ.AI
API providerMiniMaxZ.ai
Input context204,800 tokens1,048,576 tokens
Maximum output131,072 tokens131,072 tokens
Released——
Added to WritingmateOct 23, 2025Aug 18, 2026
LicenseNot availableNot available
Knowledge cutoff——
Capabilities
InputsTextText
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionNoNo
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.255$1.40
Output (per 1M tokens)$1.02$4.40
Blended 3:1 input/output$0.44625$2.15
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 Aug 18, 2026.

Why Pay for Multiple Subscriptions?

Comparing MiniMax M2 from MiniMax with GLM 5.3 from Z.AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for MiniMax M2 and GLM 5.3
PlanPriceMiniMax M2GLM 5.3AI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
View Plans & Pricing

MiniMax M2 vs GLM 5.3 FAQ

Which is better, MiniMax M2 or GLM 5.3?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. MiniMax M2 is about 4.8× cheaper on a blended 3:1 input/output basis ($0.44625 vs $2.15 per 1M tokens). GLM 5.3 has the larger context window (1,048,576 tokens vs 204,800 tokens).

Which model is cheaper to use through an API?

MiniMax M2 is about 4.8× cheaper on a blended 3:1 input/output basis ($0.44625 vs $2.15 per 1M tokens).

Which model supports more context?

GLM 5.3 has the larger context window (1,048,576 tokens vs 204,800 tokens).

Can I switch between MiniMax M2 and GLM 5.3?

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.