Claude Haiku 4.5
Anthropic · released Oct 15, 2025
Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance...
Specification
- Context window
- 200K
- Max output
- 64K
- Knowledge cutoff
- Not stated
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 4
- Moderated
- Yes
- Uptime
- 100.0%
Intelligence
24.1
66th percentile
Coding
—
Coding Index
Agentic
16.5
Agentic Index
Output speed
111 t/s
Median across providers
Latency
487ms
Time to first token
Cost per task
$0.45
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- MMLU-Pro80.0%
- GPQA Diamond64.6%
- LiveCodeBench51.1%
- AA-LCR (long context)48.3%
- IFBench42.0%
- AIME 202539.0%
- SciCode34.4%
- τ²-bench (Telecom)32.5%
- Terminal-Bench Hard27.3%
- Humanity's Last Exam4.2%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 80.0% |
| GPQA Diamond | 64.6% |
| LiveCodeBench | 51.1% |
| AA-LCR (long context) | 48.3% |
| IFBench | 42.0% |
| AIME 2025 | 39.0% |
| SciCode | 34.4% |
| τ²-bench (Telecom) | 32.5% |
| Terminal-Bench Hard | 27.3% |
| Humanity's Last Exam | 4.2% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Qwen3 MaxQwen24.5
- Ling 3.0 Tiny (free)InclusionAI24.3
- Claude Haiku 4.5Anthropic24.1
- gpt-oss-120bOpenAI24.1
- Kimi K2 0905Moonshot AI24.0
- o1OpenAI23.9
- GLM 4.6Z.ai23.4
- GLM 4.7 FlashZ.ai23.3
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Qwen3 Max | 24.5 |
| Ling 3.0 Tiny (free) | 24.3 |
| Claude Haiku 4.5 | 24.1 |
| gpt-oss-120b | 24.1 |
| Kimi K2 0905 | 24.0 |
| o1 | 23.9 |
| GLM 4.6 | 23.4 |
| GLM 4.7 Flash | 23.3 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $1
- Output / 1M tokens
- $5
- Cached input / 1M
- $0.1
- Blended 3:1
- $2
One task, estimated
$0.45
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.
Arena
Head-to-head generation quality
Elo from pairwise judgements, broken out by the kind of thing the model was asked to build.
- Overall Elo
- 1,151
- Win rate
- 45.6%
- Strongest at
- Data visualisation
- Tournaments
- 1,131
Elo by category
Dot position on a 1,050–1,175 scale · Elo has no meaningful zero
- 1,151
- 1,145
- 1,138
- 1,134
- 1,127
- 1,072
Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.
View as table
| Category | Elo | Win rate |
|---|---|---|
| Data visualisation | 1151 | 45.6% |
| Websites | 1145 | 45.1% |
| Game development | 1138 | 44.6% |
| UI components | 1134 | 42.7% |
| 3D scenes | 1127 | 41.1% |
| SVG | 1072 | 39.1% |