Claude Haiku 3.5 vs Gemini 2.5 Flash
| Spec | Claude Haiku 4.5 | Gemini 2.5 Flash |
|---|---|---|
| Provider | Anthropic | |
| Input price (per 1M) | $1.00 | $0.30 |
| Output price (per 1M) | $5.00 | $2.50 |
| Context window | 200,000 | 1,000,000 |
| Tokenizer accuracy | exact (uses official tokenizer) | exact (uses official tokenizer) |
Cost per 1,000 calls across common workloads
| Workload | Claude Haiku 4.5 | Gemini 2.5 Flash | Winner |
|---|---|---|---|
| Short chat (200 in / 100 out) |
$700.00 | $310.00 | Gemini 2.5 Flash 56% cheaper |
| Medium chat (1,000 in / 500 out) |
$3,500.00 | $1,550.00 | Gemini 2.5 Flash 56% cheaper |
| Heavy generation (1,000 in / 2,000 out) |
$11,000.00 | $5,300.00 | Gemini 2.5 Flash 52% cheaper |
| Long context (8,000 in / 500 out) |
$10,500.00 | $3,650.00 | Gemini 2.5 Flash 65% cheaper |
| Code review (3,000 in / 600 out) |
$6,000.00 | $2,400.00 | Gemini 2.5 Flash 60% cheaper |
Costs are per 1,000 API calls. Multiply by 1,000 for per-million-calls.
Verdict
Gemini 2.5 Flash wins on raw price-per-token. Claude Haiku 3.5 wins on writing quality and long-context discipline. They're aimed at the same workloads (high-volume, low-stakes inference) but optimize for different things.
If you're cost-sensitive and your task is well-bounded, Flash is the cheaper bet. If you care about output polish, customer-facing copy, support replies, anything users will read directly. Haiku's outputs read more naturally to humans.
Cost example
For a 1,000-token prompt with a 200-token reply:
Claude Haiku 3.5: 1000 × $0.80/M + 200 × $4/M = $0.00160 per call
Gemini 2.5 Flash: 1000 × $0.30/M + 200 × $2.50/M = $0.00080 per callFlash costs ~2× less per call. At 10M calls/month: $16,000 vs $8,000, an $8,000 monthly difference.
Context windows
- Claude Haiku 3.5: 200,000 tokens
- Gemini 2.5 Flash: 1,000,000 tokens
Gemini Flash's 1M context is genuinely useful for whole-codebase or multi-document tasks where you can't easily pre-filter. Haiku's 200k is plenty for typical RAG.
Quality differences
Where Flash leads:
- Per-token cost (significant at scale)
- Multimodal cost. Flash's image tokens are ~258 per image flat; Haiku scales by pixel area
- Raw throughput on the Google AI platform
Where Haiku leads:
- Conversational writing, Haiku reads as more natural in customer-facing replies
- Instruction-following on multi-step prompts
- Long-context recall accuracy (less "lost in the middle" drift)
- Code generation for short refactors
On benchmark scores (MMLU, HumanEval), the two are within a few points of each other. The difference is more about output style. Flash is fast and accurate, Haiku feels more "edited."
Tokenizers
Claude's tokenizer is ~30% less efficient than Gemini's on the same English text. So even though Haiku's per-token price is only 2.6× Flash's input price, the effective cost-per-input-character is closer to 3.4×, slightly worse than the per-token ratio suggests.
For purely English text workloads, this widens Flash's cost advantage further. For multilingual or code-heavy text, the gap narrows.
When to choose each
Use Gemini 2.5 Flash when:
- Cost matters more than output style
- You need 1M+ context
- You're processing images at high volume (cheapest vision tokens of any major provider)
- The task is structured (classification, extraction, summarization)
Use Claude Haiku 3.5 when:
- You're writing anything users will read (support, marketing, FAQ)
- You need clean instruction-following on prompts with multiple constraints
- You're already in the Anthropic ecosystem
- Output quality matters more than the 2× price gap
Count tokens on Claude Haiku → · Count tokens on Gemini 2.5 Flash →