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Model Comparison

Claude Haiku 5.5 vs Kimi K2 0711Which Is Better in 2026?

Claude Haiku 5.5 vs Kimi K2 0711: which should you choose in 2026?

Claude Haiku 5.5 (by Anthropic) and Kimi K2 0711 (by Moonshot AI) 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.

At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.

Claude Haiku 5.5 has the larger context window (1,000,000 tokens vs 131,072 tokens).

Choose Claude Haiku 5.5 if…

  • • you work with longer documents, transcripts, or codebases
  • • you need a model with explicit reasoning support
  • • you need image inputs

Choose Kimi K2 0711 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.

vs

Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

Claude Haiku 5.5 and Kimi K2 0711 benchmark results
BenchmarkClaude Haiku 5.5Kimi K2 0711
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

——
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

Claude Haiku 5.5 and Kimi K2 0711 model facts
FeatureClaude Haiku 5.5Kimi K2 0711
Context & model facts
DeveloperAnthropicMoonshot AI
API providerAnthropicMoonshotAI
Input context1,000,000 tokens131,072 tokens
Maximum output128,000 tokens98,304 tokens
Released——
Added to WritingmateOct 7, 2026Jul 11, 2025
LicenseNot availableNot available
Knowledge cutoff—2024-12-31
Capabilities
InputsText, Image, FileText
OutputsTextText
Provider endpoint accepts tool parametersYesYes
ReasoningYesNo
VisionYesNo
Image GenerationNoNo
Video GenerationNoNo
API pricing
Base input (per 1M tokens)$0.10$0.57
Base output (per 1M tokens)$0.50$2.30
Higher-context pricing tiers
  • ≥100,000 prompt tokens$0.50 input · $2.50 output per 1M
Base rate only
Price-comparison caveatAt least one model changes token rates above a prompt-token threshold. Base rates are shown, but an unconditional blended price ratio would not be like-for-like.
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 Oct 7, 2026.

Why Pay for Multiple Subscriptions?

Comparing Claude Haiku 5.5 from Anthropic with Kimi K2 0711 from Moonshot AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Claude Haiku 5.5 and Kimi K2 0711
PlanPriceClaude Haiku 5.5Kimi K2 0711AI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0

Claude Haiku 5.5 vs Kimi K2 0711 FAQ

Which is better, Claude Haiku 5.5 or Kimi K2 0711?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison. Claude Haiku 5.5 has the larger context window (1,000,000 tokens vs 131,072 tokens).

Which model is cheaper to use through an API?

At least one model uses context-dependent token-pricing tiers, so the published base rates do not support an unconditional blended price comparison.

Which model supports more context?

Claude Haiku 5.5 has the larger context window (1,000,000 tokens vs 131,072 tokens).

Can I switch between Claude Haiku 5.5 and Kimi K2 0711?

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.