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Model Comparison

Claude Sonnet 5 vs GPT-5.6 LunaWhich Is Better in 2026?

Claude Sonnet 5 vs GPT-5.6 Luna: which should you choose in 2026?

Claude Sonnet 5 (by Anthropic) and GPT-5.6 Luna (by OpenAI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

Claude Sonnet 5 outperforms in 1 benchmark (Humanity's Last Exam), while GPT-5.6 Luna is better at 2 benchmarks (AutomationBench-AA, DeepSWE 1.1).

GPT-5.6 Luna is about 17.8× cheaper on a blended 3:1 input/output basis ($0.23 vs $4.00 per 1M tokens).

Choose Claude Sonnet 5 if…

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

Choose GPT-5.6 Luna if…

  • you want the model leading more of the 4 shared benchmarks
  • 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.

Claude Sonnet 5 and GPT-5.6 Luna benchmark results
BenchmarkClaude Sonnet 5GPT-5.6 Luna
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

DeepSWE 1.1

A long-horizon software-engineering benchmark with 113 original tasks graded by hand-written tests.

Pass@1

54.0%±4.0
DataCurve
67.0%±4.0
DataCurve
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

Protocols differ or are incompletely disclosed; not counted as a controlled win

74.6%±1.6
Terminal-Bench
75.7%±1.3
Terminal-Bench
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

LiveBench

A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol.

Mean of category averages

Protocols differ or are incompletely disclosed; not counted as a controlled win

Pricing, capabilities, and model facts

Claude Sonnet 5 and GPT-5.6 Luna model facts
FeatureClaude Sonnet 5GPT-5.6 Luna
Context & model facts
DeveloperAnthropicOpenAI
API providerAnthropicOpenAI
Input context1,000,000 tokens1,050,000 tokens
Maximum output128,000 tokens128,000 tokens
Released
Added to WritingmateJun 30, 2026Jul 9, 2026
License
Knowledge cutoff2026-02-16
Capabilities
InputsText, Image, FileFile, Image, Text
OutputsTextText
Tool useYesYes
ReasoningYesYes
VisionYesYes
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$2.00$0.10
Output (per 1M tokens)$10.00$0.60
Blended 3:1 input/output$4.00$0.22
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 Jul 9, 2026.

Why Pay for Multiple Subscriptions?

Comparing Claude Sonnet 5 from Anthropic with GPT-5.6 Luna from OpenAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Claude Sonnet 5 and GPT-5.6 Luna
PlanPriceClaude Sonnet 5GPT-5.6 LunaAI 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

Claude Sonnet 5 vs GPT-5.6 Luna FAQ

Which is better, Claude Sonnet 5 or GPT-5.6 Luna?

Claude Sonnet 5 outperforms in 1 benchmark (Humanity's Last Exam), while GPT-5.6 Luna is better at 2 benchmarks (AutomationBench-AA, DeepSWE 1.1). GPT-5.6 Luna is about 17.8× cheaper on a blended 3:1 input/output basis ($0.23 vs $4.00 per 1M tokens).

Which model is cheaper to use through an API?

GPT-5.6 Luna is about 17.8× cheaper on a blended 3:1 input/output basis ($0.23 vs $4.00 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 Claude Sonnet 5 and GPT-5.6 Luna?

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.