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Gemini 3.5 Flash vs Claude Haiku 4.5

Side-by-side comparison of pricing, context window, modalities, licensing, and strengths — with practical guidance on which model fits which workload.

SpecGemini 3.5 FlashClaude Haiku 4.5
ProviderGoogleAnthropic
Context window1M tokens200K tokens
Input price (per 1M tokens)$1.50$1.00
Output price (per 1M tokens)$9.00$5.00
Example workload (10M in + 2M out)$33.00 / month$20.00 / month
Modalitiestext, image, audio, video, codetext, image, code
Knowledge cutoffNot disclosedFeb 2025
Release date2026Oct 2025
LicenseProprietaryProprietary
StrengthsAgentic coding, Speed, Long context, ValueFastest Claude, Low cost, Coding, Sub-agents
Open weights No No

Verdict

The fast-tier fight. Gemini 3.5 Flash is no longer just a budget model — it posts strong agentic-coding benchmark scores and handles video and audio input across a 1M-token context at $1.50/$9. Claude Haiku 4.5 is cheaper ($1/$5), extremely fast, and punches above its size on coding, but its context stops at 200K tokens and it takes text and images only. Flash is the more capable generalist; Haiku is the leaner, cheaper specialist for high-volume sub-tasks.

When to choose Gemini 3.5 Flash

Pick Gemini 3.5 Flash for multimodal inputs, million-token context, and agentic coding on a mid-range budget.

When to choose Claude Haiku 4.5

Pick Claude Haiku 4.5 for the lowest cost per request, fast sub-agent work, and high-volume classification or extraction.

Prices and specs reflect published provider information and change frequently — always confirm on the provider's pricing page before committing to a workload.

Frequently asked questions

Which is cheaper: Gemini 3.5 Flash or Claude Haiku 4.5?

Claude Haiku 4.5 is cheaper per token: $1.00 input / $5.00 output per 1M tokens, versus $1.50 / $9.00 for Gemini 3.5 Flash. Actual costs depend on your input-to-output ratio — try the AI cost calculator for your own numbers.

Which has the larger context window?

Gemini 3.5 Flash supports 1M tokens versus 200K for Claude Haiku 4.5 — roughly 5.2× more room for documents, code, and conversation history.

Can I self-host either model?

No — both models are proprietary and available only through their providers' APIs or cloud platforms.

How should I test which model is better for my use case?

Benchmarks are a starting point, not an answer. Run both models on 20–50 examples of your real task and compare outputs blind. Use our token counter to estimate prompt sizes and the cost calculator to project monthly spend before committing.