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Model Comparison

Ling 3.1 Flash vs GPT-5 MiniWhich Is Better in 2026?

inclusionAI•OpenAI: GPT-5 Mini logoOpenAI

Ling 3.1 Flash vs GPT-5 Mini: which should you choose in 2026?

Ling 3.1 Flash (by inclusionAI) and GPT-5 Mini (by OpenAI) 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.

Ling 3.1 Flash has no token charge in the catalog; the other model costs $0.6875 per 1M tokens on a blended 3:1 input/output basis.

GPT-5 Mini has the larger context window (400,000 tokens vs 262,144 tokens).

Choose Ling 3.1 Flash if…

  • • lower blended API cost matters for your workload

Choose GPT-5 Mini 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.

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.

Ling 3.1 Flash and GPT-5 Mini benchmark results
BenchmarkLing 3.1 FlashGPT-5 Mini
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

—
59.8%
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

——

Benchmark sources

Reviewed evidence last updated 2026-08-09.

Pricing, capabilities, and model facts

Ling 3.1 Flash and GPT-5 Mini model facts
FeatureLing 3.1 FlashGPT-5 Mini
Context & model facts
DeveloperinclusionAIOpenAI
API providerinclusionAIOpenAI
Input context262,144 tokens400,000 tokens
Maximum output32,768 tokens128,000 tokens
Released——
Added to WritingmateOct 2, 2026Aug 7, 2025
LicenseNot availableNot available
Knowledge cutoff—2024-05-31
Capabilities
InputsTextText, Image, File
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionNoYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.00$0.25
Output (per 1M tokens)$0.00$2.00
Blended 3:1 input/output$0.00$0.6875
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 2, 2026.

Why Pay for Multiple Subscriptions?

Comparing Ling 3.1 Flash from inclusionAI with GPT-5 Mini from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Ling 3.1 Flash and GPT-5 Mini
PlanPriceLing 3.1 FlashGPT-5 MiniAI 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

Ling 3.1 Flash vs GPT-5 Mini FAQ

Which is better, Ling 3.1 Flash or GPT-5 Mini?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Ling 3.1 Flash has no token charge in the catalog; the other model costs $0.6875 per 1M tokens on a blended 3:1 input/output basis. GPT-5 Mini has the larger context window (400,000 tokens vs 262,144 tokens).

Which model is cheaper to use through an API?

Ling 3.1 Flash has no token charge in the catalog; the other model costs $0.6875 per 1M tokens on a blended 3:1 input/output basis.

Which model supports more context?

GPT-5 Mini has the larger context window (400,000 tokens vs 262,144 tokens).

Can I switch between Ling 3.1 Flash and GPT-5 Mini?

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.