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Model Comparison

Ling 3.0 Flash VL vs SonarWhich Is Better in 2026?

inclusionAI•Perplexity: Sonar logoPerplexity

Ling 3.0 Flash VL vs Sonar: which should you choose in 2026?

Ling 3.0 Flash VL (by inclusionAI) and Sonar (by Perplexity) 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 3.0 Flash VL is about 11.1× cheaper on a blended 3:1 input/output basis ($0.09 vs $1.00 per 1M tokens).

Choose Ling 3.0 Flash VL if…

  • • lower blended API cost matters for your workload
  • • you need a model with explicit reasoning support

Choose Sonar 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.

Ling 3.0 Flash VL and Sonar benchmark results
BenchmarkLing 3.0 Flash VLSonar
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

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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

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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

——

Benchmark sources

    Reviewed evidence last updated 2026-08-09.

    Pricing, capabilities, and model facts

    Ling 3.0 Flash VL and Sonar model facts
    FeatureLing 3.0 Flash VLSonar
    Context & model facts
    DeveloperinclusionAIPerplexity
    API providerinclusionAIPerplexity
    Input context131,072 tokens127,072 tokens
    Maximum output32,768 tokens114,364 tokens
    Released——
    Added to WritingmateSep 10, 2026Jan 27, 2025
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsText, Image, VideoText, Image
    OutputsTextText
    Provider endpoint accepts tool parametersYesNo
    ReasoningYesNo
    VisionYesYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.06$1.00
    Output (per 1M tokens)$0.18$1.00
    Blended 3:1 input/output$0.09$1.00
    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 Sep 10, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Ling 3.0 Flash VL from inclusionAI with Sonar from Perplexity? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Ling 3.0 Flash VL and Sonar
    PlanPriceLing 3.0 Flash VLSonarAI 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

    Ling 3.0 Flash VL vs Sonar FAQ

    Which is better, Ling 3.0 Flash VL or Sonar?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Ling 3.0 Flash VL is about 11.1× cheaper on a blended 3:1 input/output basis ($0.09 vs $1.00 per 1M tokens).

    Which model is cheaper to use through an API?

    Ling 3.0 Flash VL is about 11.1× cheaper on a blended 3:1 input/output basis ($0.09 vs $1.00 per 1M tokens).

    Which model supports more context?

    The published context windows are close enough that this page does not treat the difference as a material advantage.

    Can I switch between Ling 3.0 Flash VL and Sonar?

    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.