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Model Comparison

Gemini 2.5 Flash vs Ling 3.1 FlashWhich Is Better in 2026?

Google: Gemini 2.5 Flash logoGoogle•
inclusionAI

Gemini 2.5 Flash vs Ling 3.1 Flash: which should you choose in 2026?

Gemini 2.5 Flash (by Google) and Ling 3.1 Flash (by inclusionAI) 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 $0.85 per 1M tokens on a blended 3:1 input/output basis.

Gemini 2.5 Flash has the larger context window (1,048,576 tokens vs 262,144 tokens).

Choose Gemini 2.5 Flash if…

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

Choose Ling 3.1 Flash if…

  • • lower blended API cost matters for your workload

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.

Gemini 2.5 Flash and Ling 3.1 Flash benchmark results
BenchmarkGemini 2.5 FlashLing 3.1 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

——
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

28.7%
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

1409 ±2
—

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Oct 2, 2026.

Pricing, capabilities, and model facts

Gemini 2.5 Flash and Ling 3.1 Flash model facts
FeatureGemini 2.5 FlashLing 3.1 Flash
Context & model facts
DeveloperGoogleinclusionAI
API providerGoogleinclusionAI
Input context1,048,576 tokens262,144 tokens
Maximum output65,535 tokens32,768 tokens
Released——
Added to WritingmateJun 17, 2025Oct 2, 2026
LicenseNot availableNot available
Knowledge cutoff2025-01-31—
Capabilities
InputsFile, Image, Text, Audio, VideoText
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesNo
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.30$0.00
Output (per 1M tokens)$2.50$0.00
Blended 3:1 input/output$0.85$0.00
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 Gemini 2.5 Flash from Google with Ling 3.1 Flash from inclusionAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Gemini 2.5 Flash and Ling 3.1 Flash
PlanPriceGemini 2.5 FlashLing 3.1 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

Gemini 2.5 Flash vs Ling 3.1 Flash FAQ

Which is better, Gemini 2.5 Flash or Ling 3.1 Flash?

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 $0.85 per 1M tokens on a blended 3:1 input/output basis. Gemini 2.5 Flash has the larger context window (1,048,576 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 $0.85 per 1M tokens on a blended 3:1 input/output basis.

Which model supports more context?

Gemini 2.5 Flash has the larger context window (1,048,576 tokens vs 262,144 tokens).

Can I switch between Gemini 2.5 Flash and Ling 3.1 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.