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Model Comparison

Granite 4.2 8B vs MiniMax M3.1 FlashWhich Is Better in 2026?

IBM Granite•MiniMax: MiniMax M3.1 Flash logoMiniMax

Granite 4.2 8B vs MiniMax M3.1 Flash: which should you choose in 2026?

Granite 4.2 8B (by IBM Granite) and MiniMax M3.1 Flash (by MiniMax) 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.

Granite 4.2 8B is about 2.4× cheaper on a blended 3:1 input/output basis ($0.1075 vs $0.2625 per 1M tokens).

MiniMax M3.1 Flash has the larger context window (1,048,576 tokens vs 131,072 tokens).

Choose Granite 4.2 8B if…

  • • lower blended API cost matters for your workload

Choose MiniMax M3.1 Flash if…

  • • you work with longer documents, transcripts, or codebases
  • • you need image inputs

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.

Granite 4.2 8B and MiniMax M3.1 Flash benchmark results
BenchmarkGranite 4.2 8BMiniMax M3.1 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

——
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

——
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

1288 ±11
—

Benchmark sources

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

Pricing, capabilities, and model facts

Granite 4.2 8B and MiniMax M3.1 Flash model facts
FeatureGranite 4.2 8BMiniMax M3.1 Flash
Context & model facts
DeveloperIBM GraniteMiniMax
API providerIBMMiniMax
Input context131,072 tokens1,048,576 tokens
Maximum output117,964 tokens524,288 tokens
Released——
Added to WritingmateAug 31, 2026Oct 19, 2026
LicenseNot availableNot available
Knowledge cutoff——
Capabilities
InputsTextText, Image, Video
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionNoYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.06$0.15
Output (per 1M tokens)$0.25$0.60
Blended 3:1 input/output$0.1075$0.2625
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 Granite 4.2 8B from IBM Granite with MiniMax M3.1 Flash from MiniMax? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Granite 4.2 8B and MiniMax M3.1 Flash
PlanPriceGranite 4.2 8BMiniMax M3.1 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

Granite 4.2 8B vs MiniMax M3.1 Flash FAQ

Which is better, Granite 4.2 8B or MiniMax M3.1 Flash?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Granite 4.2 8B is about 2.4× cheaper on a blended 3:1 input/output basis ($0.1075 vs $0.2625 per 1M tokens). MiniMax M3.1 Flash has the larger context window (1,048,576 tokens vs 131,072 tokens).

Which model is cheaper to use through an API?

Granite 4.2 8B is about 2.4× cheaper on a blended 3:1 input/output basis ($0.1075 vs $0.2625 per 1M tokens).

Which model supports more context?

MiniMax M3.1 Flash has the larger context window (1,048,576 tokens vs 131,072 tokens).

Can I switch between Granite 4.2 8B and MiniMax M3.1 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.