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Model Comparison

MiniMax M3.1 Flash vs Qwen2.5 Coder 32B InstructWhich Is Better in 2026?

MiniMax: MiniMax M3.1 Flash logoMiniMax•Qwen2.5 Coder 32B Instruct logoQwen

MiniMax M3.1 Flash vs Qwen2.5 Coder 32B Instruct: which should you choose in 2026?

MiniMax M3.1 Flash (by MiniMax) and Qwen2.5 Coder 32B Instruct (by Qwen) 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 M3.1 Flash is about 2.8× cheaper on a blended 3:1 input/output basis ($0.2625 vs $0.745 per 1M tokens).

MiniMax M3.1 Flash has the larger context window (1,048,576 tokens vs 32,768 tokens).

Choose MiniMax M3.1 Flash if…

  • • lower blended API cost matters for your workload
  • • you work with longer documents, transcripts, or codebases
  • • you need a model with explicit reasoning support
  • • you need image inputs

Choose Qwen2.5 Coder 32B Instruct 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.

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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 M3.1 Flash and Qwen2.5 Coder 32B Instruct benchmark results
BenchmarkMiniMax M3.1 FlashQwen2.5 Coder 32B Instruct
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

——
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

—
9.0%
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

——

Benchmark sources

Reviewed evidence last updated 2026-08-09.

Pricing, capabilities, and model facts

MiniMax M3.1 Flash and Qwen2.5 Coder 32B Instruct model facts
FeatureMiniMax M3.1 FlashQwen2.5 Coder 32B Instruct
Context & model facts
DeveloperMiniMaxQwen
API providerMiniMaxQwen2.5 Coder 32B Instruct
Input context1,048,576 tokens32,768 tokens
Maximum output524,288 tokens29,491 tokens
Released——
Added to WritingmateOct 19, 2026Nov 11, 2024
LicenseNot availableNot available
Knowledge cutoff—2024-06-30
Capabilities
InputsText, Image, VideoText
OutputsTextText
Provider endpoint accepts tool parametersYesNo
ReasoningYesNo
VisionYesNo
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.15$0.66
Output (per 1M tokens)$0.60$1.00
Blended 3:1 input/output$0.2625$0.745
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 Oct 19, 2026.

Why Pay for Multiple Subscriptions?

Comparing MiniMax M3.1 Flash from MiniMax with Qwen2.5 Coder 32B Instruct from Qwen? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for MiniMax M3.1 Flash and Qwen2.5 Coder 32B Instruct
PlanPriceMiniMax M3.1 FlashQwen2.5 Coder 32B InstructAI 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

MiniMax M3.1 Flash vs Qwen2.5 Coder 32B Instruct FAQ

Which is better, MiniMax M3.1 Flash or Qwen2.5 Coder 32B Instruct?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. MiniMax M3.1 Flash is about 2.8× cheaper on a blended 3:1 input/output basis ($0.2625 vs $0.745 per 1M tokens). MiniMax M3.1 Flash has the larger context window (1,048,576 tokens vs 32,768 tokens).

Which model is cheaper to use through an API?

MiniMax M3.1 Flash is about 2.8× cheaper on a blended 3:1 input/output basis ($0.2625 vs $0.745 per 1M tokens).

Which model supports more context?

MiniMax M3.1 Flash has the larger context window (1,048,576 tokens vs 32,768 tokens).

Can I switch between MiniMax M3.1 Flash and Qwen2.5 Coder 32B Instruct?

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.