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Model Comparison

o4 Mini vs R1Which Is Better in 2026?

OpenAI: o4 Mini logoOpenAI•DeepSeek: R1 logoDeepSeek

o4 Mini vs R1: which should you choose in 2026?

o4 Mini (by OpenAI) and R1 (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.

R1 is about 1.7× cheaper on a blended 3:1 input/output basis ($1.15 vs $1.93 per 1M tokens).

o4 Mini has the larger context window (200,000 tokens vs 64,000 tokens).

Choose o4 Mini if…

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

Choose R1 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.

o4 Mini and R1 benchmark results
Benchmarko4 MiniR1
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

45.0%
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

——
AIME 2025

30 problems from the 2025 American Invitational Mathematics Examination testing olympiad-level math reasoning

Accuracy %

32.65
matharena.ai
—
MathArena Apex

Challenging math contest benchmark featuring the most difficult olympiad-level problems

Accuracy %

32.65
matharena.ai
—
Arena preference scores
Arena (Text)

Human preference score

—
1398 ±5

Benchmark sources

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

Pricing, capabilities, and model facts

o4 Mini and R1 model facts
Featureo4 MiniR1
Context & model facts
DeveloperOpenAIDeepSeek
API providerOpenAIDeepSeek
Input context200,000 tokens64,000 tokens
Maximum output100,000 tokens16,000 tokens
Released——
Added to WritingmateApr 16, 2025Jan 20, 2025
LicenseNot availableNot available
Knowledge cutoff2024-06-302024-07-31
Capabilities
InputsImage, Text, FileText
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesYes
VisionYesNo
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$1.10$0.70
Output (per 1M tokens)$4.40$2.50
Blended 3:1 input/output$1.93$1.15
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 Apr 16, 2025.

Why Pay for Multiple Subscriptions?

Comparing o4 Mini from OpenAI with R1 from DeepSeek? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for o4 Mini and R1
PlanPriceo4 MiniR1AI 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

o4 Mini vs R1 FAQ

Which is better, o4 Mini or R1?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. R1 is about 1.7× cheaper on a blended 3:1 input/output basis ($1.15 vs $1.93 per 1M tokens). o4 Mini has the larger context window (200,000 tokens vs 64,000 tokens).

Which model is cheaper to use through an API?

R1 is about 1.7× cheaper on a blended 3:1 input/output basis ($1.15 vs $1.93 per 1M tokens).

Which model supports more context?

o4 Mini has the larger context window (200,000 tokens vs 64,000 tokens).

Can I switch between o4 Mini and R1?

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.