text model comparison
Claude Haiku 5.5 vs DeepSeek-V4.1-Flash
Claude Haiku 5.5 and DeepSeek-V4.1-Flash are both current text models, from Anthropic and DeepSeek. Claude Haiku 5.5 is 67% cheaper per 1M input tokens than DeepSeek-V4.1-Flash, $0.10 vs $0.30. On GPQA Diamond, Claude Haiku 5.5 scores 89.6% and DeepSeek-V4.1-Flash 89.8% (Epoch AI, measured October 12, 2026). Both accept 1,000,000 tokens of context.
Data verified October 12, 2026 · Dataset 2026-10-12
Side by side
Standard-tier prices in effect today. The better value in each row is highlighted.
| Fact | Claude Haiku 5.5 | DeepSeek-V4.1-Flash |
|---|---|---|
| Price per 1M input tokens | $0.10 (better) | $0.30 |
| Price per 1M output tokens | $0.50 (better) | $1.20 |
| Cost of 10M input and 2M output tokens | $2 (better) | $5.40 |
| GPQA Diamond | 89.6% (#26 of 37) | 89.8% (#25 of 37) (better) |
| FrontierMath Tiers 1-3 (v2) | 75.1% (#16 of 37) (better) | 67.4% (#22 of 37) |
| SimpleQA Verified | 23.8% (#31 of 34) | Not measured |
| Arena Text | Not measured | 1475 Elo (#18 of 39) |
| Provider | Anthropic | DeepSeek |
| Release date | October 7, 2026 | September 10, 2026 |
| Access | API only | API and open weights |
| Context window | 1,000,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | 384,000 tokens |
| Input | text, image | text, image |
| Output | text | text |
When to choose each
Claude Haiku 5.5
Best for
- High-volume classification, extraction and routing under the 100,000-token line
- Cost-sensitive fan-out steps where a frontier model is overkill
Watch out for
- Priced by prompt length, and it is a cliff rather than a taper: a request whose prompt passes 100,000 tokens is billed at $0.50 input and $2.50 output for the whole request, five times the short rate
- Cache reads and cache writes both count toward that 100,000-token measurement, so a large cached prefix can tip a request into the higher band on its own
- It is the only current Claude model whose 1M window is not priced uniformly; every other 4.6-and-later model bills a 900k request at the same rate as a 9k one
DeepSeek-V4.1-Flash
Best for
- High-volume agent and multimodal workloads where per-token cost dominates
- Teams that want a frontier-adjacent model they can also self-host under MIT terms
Watch out for
- The recorded API rates are the peak-hour prices; DeepSeek charges half outside 01:00-04:00 and 06:00-10:00 UTC on weekdays
- Running the open weights moves cost to your own infrastructure, which the token rates here do not represent
Claude Haiku 5.5 vs DeepSeek-V4.1-Flash FAQ
Which is cheaper, Claude Haiku 5.5 or DeepSeek-V4.1-Flash?
Claude Haiku 5.5 is 67% cheaper per 1M input tokens than DeepSeek-V4.1-Flash, $0.10 vs $0.30. For 10M input and 2M output tokens, Claude Haiku 5.5 costs $2 and DeepSeek-V4.1-Flash $5.40 at standard rates.
Which scores higher on independent benchmarks, Claude Haiku 5.5 or DeepSeek-V4.1-Flash?
DeepSeek-V4.1-Flash leads on GPQA Diamond, 89.8% to 89.6%; Claude Haiku 5.5 leads on FrontierMath Tiers 1-3 (v2), 75.1% to 67.4%. Scores are independent evaluations, not either vendor's launch figures.
Which has the larger context window, Claude Haiku 5.5 or DeepSeek-V4.1-Flash?
Both accept 1,000,000 tokens of context.
Can I run Claude Haiku 5.5 or DeepSeek-V4.1-Flash myself?
DeepSeek-V4.1-Flash is released as open weights you can run yourself; Claude Haiku 5.5 is available through its provider's API only.
Related comparisons
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- Claude Haiku 5.5 vs GPT-6 Luna
- Claude Haiku 5.5 vs Qwen3.8-Omni-Flash
- Claude Sonnet 5 vs DeepSeek-V4.1-Flash
- DeepSeek-V4.1-Flash vs Gemini 3.1 Flash-Lite
- DeepSeek-V4.1-Flash vs Gemini 3.5 Flash-Lite
- DeepSeek-V4.1-Flash vs Gemini 3.7 Flash
- DeepSeek-V4.1-Flash vs Gemini 3.8 Flash
- DeepSeek-V4.1-Flash vs Gemini 3 Flash Preview
Production evaluation
Choosing between Claude Haiku 5.5 and DeepSeek-V4.1-Flash?
Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.
- A head-to-head evaluation on your real workload
- Routing between models by cost and difficulty
