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Model Comparison

Nova Premier 1.0 vs DeepSeek V4 Flash Vision ExpWhich Is Better in 2026?

Amazon•DeepSeek: DeepSeek V4 Flash Vision Exp logoDeepSeek

Nova Premier 1.0 vs DeepSeek V4 Flash Vision Exp: which should you choose in 2026?

Nova Premier 1.0 (by Amazon) and DeepSeek V4 Flash Vision Exp (by DeepSeek) 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.

DeepSeek V4 Flash Vision Exp is about 15.2× cheaper on a blended 3:1 input/output basis ($0.33 vs $5.00 per 1M tokens).

Choose Nova Premier 1.0 if…

  • • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage

Choose DeepSeek V4 Flash Vision Exp if…

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

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.

Nova Premier 1.0 and DeepSeek V4 Flash Vision Exp benchmark results
BenchmarkNova Premier 1.0DeepSeek V4 Flash Vision Exp
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

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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

    Nova Premier 1.0 and DeepSeek V4 Flash Vision Exp model facts
    FeatureNova Premier 1.0DeepSeek V4 Flash Vision Exp
    Context & model facts
    DeveloperAmazonDeepSeek
    API providerAmazonDeepSeek
    Input context1,000,000 tokens1,048,576 tokens
    Maximum output32,000 tokens384,000 tokens
    Released——
    Added to WritingmateOct 31, 2025Aug 21, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsText, ImageText, Image
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningNoYes
    VisionYesYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$2.50$0.22
    Output (per 1M tokens)$12.50$0.66
    Blended 3:1 input/output$5.00$0.33
    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 Aug 21, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Nova Premier 1.0 from Amazon with DeepSeek V4 Flash Vision Exp from DeepSeek? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Nova Premier 1.0 and DeepSeek V4 Flash Vision Exp
    PlanPriceNova Premier 1.0DeepSeek V4 Flash Vision ExpAI 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

    Nova Premier 1.0 vs DeepSeek V4 Flash Vision Exp FAQ

    Which is better, Nova Premier 1.0 or DeepSeek V4 Flash Vision Exp?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. DeepSeek V4 Flash Vision Exp is about 15.2× cheaper on a blended 3:1 input/output basis ($0.33 vs $5.00 per 1M tokens).

    Which model is cheaper to use through an API?

    DeepSeek V4 Flash Vision Exp is about 15.2× cheaper on a blended 3:1 input/output basis ($0.33 vs $5.00 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 Nova Premier 1.0 and DeepSeek V4 Flash Vision Exp?

    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.