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Model Comparison

R1 0528 vs Gemini 3.7 FlashWhich Is Better in 2026?

DeepSeek: R1 0528 logoDeepSeek•Google: Gemini 3.7 Flash logoGoogle

R1 0528 vs Gemini 3.7 Flash: which should you choose in 2026?

R1 0528 (by DeepSeek) and Gemini 3.7 Flash (by Google) 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.

Gemini 3.7 Flash is about 1.2× cheaper on a blended 3:1 input/output basis ($0.75 vs $0.9125 per 1M tokens).

Gemini 3.7 Flash has the larger context window (1,048,576 tokens vs 163,840 tokens).

Choose R1 0528 if…

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

Choose Gemini 3.7 Flash if…

  • • lower blended API cost matters for your workload
  • • 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.

R1 0528 and Gemini 3.7 Flash benchmark results
BenchmarkR1 0528Gemini 3.7 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

1422 ±6
—

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 23, 2026.

Pricing, capabilities, and model facts

R1 0528 and Gemini 3.7 Flash model facts
FeatureR1 0528Gemini 3.7 Flash
Context & model facts
DeveloperDeepSeekGoogle
API providerDeepSeekGoogle
Input context163,840 tokens1,048,576 tokens
Maximum output32,768 tokens65,536 tokens
Released——
Added to WritingmateMay 28, 2025Aug 13, 2026
LicenseNot availableNot available
Knowledge cutoff2025-03-31—
Capabilities
InputsTextText, Image, Video, File, Audio
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionNoYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.50$0.375
Output (per 1M tokens)$2.15$1.88
Blended 3:1 input/output$0.9125$0.75
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 13, 2026.

Why Pay for Multiple Subscriptions?

Comparing R1 0528 from DeepSeek with Gemini 3.7 Flash from Google? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for R1 0528 and Gemini 3.7 Flash
PlanPriceR1 0528Gemini 3.7 FlashAI 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

R1 0528 vs Gemini 3.7 Flash FAQ

Which is better, R1 0528 or Gemini 3.7 Flash?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Gemini 3.7 Flash is about 1.2× cheaper on a blended 3:1 input/output basis ($0.75 vs $0.9125 per 1M tokens). Gemini 3.7 Flash has the larger context window (1,048,576 tokens vs 163,840 tokens).

Which model is cheaper to use through an API?

Gemini 3.7 Flash is about 1.2× cheaper on a blended 3:1 input/output basis ($0.75 vs $0.9125 per 1M tokens).

Which model supports more context?

Gemini 3.7 Flash has the larger context window (1,048,576 tokens vs 163,840 tokens).

Can I switch between R1 0528 and Gemini 3.7 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.