Anthropic, proprietary

# Claude Haiku 4.5

> Claude Haiku 4.5 by Anthropic, released October 2025. Ranked #165 of 354 with a Noometry Index of 39.5. API: $1 in / $5 out per M tokens. 200K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/claude-haiku-4-5
- Last updated: 2026-10-10
- Title: Claude Haiku 4.5 Benchmarks, Price & Rank (October 2026)

Claude Haiku 4.5 by Anthropic ranks 165th of 354 ranked models on the Noometry Index as of October 2026, with a score of 39.5. Its strongest category is agentic & tool use, where it ranks 52nd. API pricing starts at $1 per million input tokens and $5 per million output tokens, with a 200K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #165 of 354
- **Index score:** 39.5
- **Evidence:** Confirmed 53 results
- **Provider:** [Anthropic](https://noometry.com/providers/anthropic)
- **Released:** October 15, 2025
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 200K
- **Max output:** 64K
- **Input price:** $1 / M
- **Output price:** $5 / M
- **Blended price:** $2 / M
- **Output speed:** Not measured
- **Value:** #151 of 219
- **Knowledge cutoff:** February 2025
- **Input:** text, image, pdf

## Category scores

Each category score combines every public result we have in that category.

Claude Haiku 4.5 category scores

1.  Coding 44.0
2.  Agentic & Tool Use 33.6
3.  Reasoning 15.1
4.  Math 44.9
5.  Knowledge 37.7
6.  Multimodal 26.8
7.  Multilingual 49.9
8.  Instruction Following 71.4
9.  Long Context 43.6
10.  Writing & Preference 57.9
11.  020406080

Claude Haiku 4.5 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 44.0 | #78 | 6 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 33.6 | #52 | 5 |
| [Reasoning](https://noometry.com/best/reasoning) | 15.1 | #320 | 8 |
| [Math](https://noometry.com/best/math) | 44.9 | #78 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 37.7 | #153 | 6 |
| [Multimodal](https://noometry.com/best/multimodal) | 26.8 | #118 | 1 |
| [Multilingual](https://noometry.com/best/multilingual) | 49.9 | #129 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 71.4 | #149 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 43.6 | #92 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 57.9 | #123 | 5 |

## Strengths and weaknesses

Categories where Claude Haiku 4.5 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

Claude Haiku 4.5: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 44.0 | +5.3 | #78 of 340, top 23% |
| [Math](https://noometry.com/best/math) | 44.9 | +8.3 | #78 of 327, top 24% |
| [Long Context](https://noometry.com/best/long-context) | 43.6 | +2.7 | #92 of 296, top 32% |

### Weakest categories

Claude Haiku 4.5: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 26.8 | −11.7 | #118 of 128, top 93% |
| [Reasoning](https://noometry.com/best/reasoning) | 15.1 | −8.6 | #320 of 350, top 92% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 71.4 | +0.1 | #149 of 305, top 49% |

## Closest competitors

The models ranked just above and below Claude Haiku 4.5. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to Claude Haiku 4.5
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Nova 2 Lite](https://noometry.com/models/nova-2-lite) | #161 | 39.7 | $0.85 | — | [Compare](https://noometry.com/compare/claude-haiku-4-5-vs-nova-2-lite) |
| [DeepSeek-V3.2-Speciale](https://noometry.com/models/deepseek-v3-2-speciale) | #162 | 39.7 | $0.85 | — | [Compare](https://noometry.com/compare/claude-haiku-4-5-vs-deepseek-v3-2-speciale) |
| [Hunyuan Turbo 0110](https://noometry.com/models/hunyuan-turbo) | #163 | 39.6 | — | — | [Compare](https://noometry.com/compare/claude-haiku-4-5-vs-hunyuan-turbo) |
| [Claude 3.7 Sonnet](https://noometry.com/models/claude-3-7-sonnet) | #164 | 39.5 | — | — | [Compare](https://noometry.com/compare/claude-3-7-sonnet-vs-claude-haiku-4-5) |
| [DeepSeek-V3](https://noometry.com/models/deepseek-v3) | #166 | 39.5 | $0.41 | 73 | [Compare](https://noometry.com/compare/claude-haiku-4-5-vs-deepseek-v3) |
| [Grok 4 Fast](https://noometry.com/models/grok-4-fast) | #167 | 39.4 | — | 577 | [Compare](https://noometry.com/compare/claude-haiku-4-5-vs-grok-4-fast) |
| [Olmo 3.1 32b Instruct](https://noometry.com/models/olmo-3-1-32b-instruct) | #168 | 39.4 | — | — | [Compare](https://noometry.com/compare/claude-haiku-4-5-vs-olmo-3-1-32b-instruct) |
| [Granite 4.2 3b](https://noometry.com/models/granite-4-2-3b) | #169 | 39.4 | — | — | [Compare](https://noometry.com/compare/claude-haiku-4-5-vs-granite-4-2-3b) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

Claude Haiku 4.5 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 66.6% | #13 of 39, top 34% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1330 | #94 of 113, top 84% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SWE-bench Multilingual](https://noometry.com/benchmarks/swe-bench-multilingual) | 64.7% | #11 of 13, top 85% |  | [SWE-bench](https://www.swebench.com/) | 2026-02-13 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 43.3% | #64 of 121, top 53% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 45.4% | #61 of 119, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 44.1% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1453 | #83 of 294, top 29% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 653.48 | #75 of 105, top 72% | 32K | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Claude Haiku 4.5 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 35.5% | #28 of 41, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 68.7% | #6 of 49, top 13% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 45.5% | #15 of 24, top 63% | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 31.2% | #17 of 35, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [BALROG](https://noometry.com/benchmarks/balrog) | 31.2% |  | 1K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ExploitBench](https://noometry.com/benchmarks/exploitbench) | 13.7% | #8 of 9, top 89% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 458.89 | #51 of 60, top 85% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Claude Haiku 4.5 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 1.3% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 2.8% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 1.3% |  | 1K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 4% | #61 of 83, top 74% | 32K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 1.7% |  | 8K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 14.3% | #84 of 91, top 93% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 14.3% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 37.3% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 16.8% |  | 1K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 47.7% | #58 of 83, top 70% | 32K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 25.5% |  | 8K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 0% | #102 of 134, top 77% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 8% | #84 of 129, top 66% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-16 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1420 | #105 of 297, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 73.6% | #90 of 151, top 60% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 30.9% | #71 of 125, top 57% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 142.41 | #98 of 213, top 47% |  | [Epoch AI](https://epoch.ai/eci) | 2025-10-15 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 61.4 | #9 of 72, top 13% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Claude Haiku 4.5 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 35.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-16 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 66.7% | #97 of 173, top 57% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-22 |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 56.1% | #12 of 57, top 22% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1396 | #130 of 285, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 86.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-16 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 96.4% | #9 of 79, top 12% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-22 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 4.1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-16 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 5.9% | #43 of 68, top 64% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-22 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 2.1% | #39 of 55, top 71% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-22 |

### Knowledge

Claude Haiku 4.5 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 60.5% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-16 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 71.2% | #97 of 186, top 53% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-22 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 13.2% | #70 of 77, top 91% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 12.6% |  | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 77.7% | #25 of 58, top 44% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 9.8% | #54 of 96, top 57% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 60.5% | #23 of 57, top 41% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1442 | #81 of 273, top 30% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Claude Haiku 4.5 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Blueprint-Bench 2](https://noometry.com/benchmarks/blueprint-bench-2) | 0% | #27 of 31, top 88% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1420 | #32 of 38, top 85% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Claude Haiku 4.5 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1377 | #129 of 297, top 44% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1417 | #128 of 285, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1408 | #114 of 223, top 52% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1375 | #113 of 231, top 49% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1339 | #106 of 211, top 51% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1347 | #106 of 213, top 50% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1381 | #128 of 283, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1420 | #90 of 226, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Claude Haiku 4.5 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 80.1% | #43 of 57, top 76% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1414 | #80 of 298, top 27% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Claude Haiku 4.5 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1427 | #83 of 291, top 29% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Claude Haiku 4.5 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1396 | #122 of 297, top 42% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1372 | #109 of 295, top 37% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 83.9% | #15 of 57, top 27% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1064 | #26 of 28, top 93% |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1409 | #111 of 295, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Claude Haiku 4.5 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [anthropic](https://docs.anthropic.com/en/docs/about-claude/models) | $1 | $5 | $0.10 | 2026-10-10 |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $1 | $5 | $0.10 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $1 | $5 | $0.10 | 2026-10-10 |
| [openrouter](https://openrouter.ai/anthropic/claude-haiku-4.5) | $1 | $5 | $0.10 | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude) | $1 | $5 | $0.10 | 2026-10-10 |

[All Anthropic API prices →](https://noometry.com/llm-pricing/anthropic) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare Claude Haiku 4.5

-   [Claude Haiku 4.5 vs Claude 3.5 Haiku](https://noometry.com/compare/claude-3-5-haiku-vs-claude-haiku-4-5)
-   [Claude Haiku 4.5 vs Claude 3.7 Sonnet](https://noometry.com/compare/claude-3-7-sonnet-vs-claude-haiku-4-5)
-   [Claude Haiku 4.5 vs DeepSeek-V3](https://noometry.com/compare/claude-haiku-4-5-vs-deepseek-v3)
-   [Claude Haiku 4.5 vs Hunyuan Turbo 0110](https://noometry.com/compare/claude-haiku-4-5-vs-hunyuan-turbo)
-   [Claude Haiku 4.5 vs Grok 4 Fast](https://noometry.com/compare/claude-haiku-4-5-vs-grok-4-fast)
-   [Claude Haiku 4.5 vs DeepSeek-V3.2-Speciale](https://noometry.com/compare/claude-haiku-4-5-vs-deepseek-v3-2-speciale)
-   [Claude Haiku 4.5 vs Olmo 3.1 32b Instruct](https://noometry.com/compare/claude-haiku-4-5-vs-olmo-3-1-32b-instruct)
-   [Claude Haiku 4.5 vs GPT-6 Astra](https://noometry.com/compare/claude-haiku-4-5-vs-gpt-6-astra)
-   [Claude Haiku 4.5 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-haiku-4-5-vs-gemini-3-8-flash)
-   [Claude Haiku 4.5 vs Kimi K3](https://noometry.com/compare/claude-haiku-4-5-vs-kimi-k3)
-   [Claude Haiku 4.5 vs Grok 4.6](https://noometry.com/compare/claude-haiku-4-5-vs-grok-4-6)
-   [Claude Haiku 4.5 vs Qwen3.8 Max](https://noometry.com/compare/claude-haiku-4-5-vs-qwen3-8-max)
-   [Claude Haiku 4.5 vs GLM-5.3](https://noometry.com/compare/claude-haiku-4-5-vs-glm-5-3)
-   [Claude Haiku 4.5 vs Muse Spark 1.3](https://noometry.com/compare/claude-haiku-4-5-vs-muse-spark-1-3)

## Other Anthropic models

-   [Claude Fable 5.1](https://noometry.com/models/claude-fable-5-1)69.0
-   [Claude Opus 5.5](https://noometry.com/models/claude-opus-5-5)68.6
-   [Claude Opus 5](https://noometry.com/models/claude-opus-5)67.8
-   [Claude Fable 5](https://noometry.com/models/claude-fable-5)66.8
-   [Claude Sonnet 5.5](https://noometry.com/models/claude-sonnet-5-5)61.9
-   [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8)60.7
-   [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7)58.3
-   [Claude Opus 4.6](https://noometry.com/models/claude-opus-4-6)58.2

## Frequently asked questions

### How good is Claude Haiku 4.5?

Claude Haiku 4.5 by Anthropic ranks 165th of 354 ranked models on the Noometry Index as of October 2026, with a score of 39.5. Its strongest category is agentic & tool use, where it ranks 52nd. API pricing starts at $1 per million input tokens and $5 per million output tokens, with a 200K-token context window.

### How much does Claude Haiku 4.5 cost?

Claude Haiku 4.5 costs $1 per million input tokens and $5 per million output tokens on Anthropic's own API, with cached input at $0.10.

### What is Claude Haiku 4.5's context window?

Claude Haiku 4.5 accepts up to 200K tokens of input and can write up to 64K tokens in one response.

### Is Claude Haiku 4.5 open source?

No. Claude Haiku 4.5 is proprietary and available only through Anthropic's API and partner platforms.

### What are Claude Haiku 4.5's strengths and weaknesses?

Relative to other ranked models, Claude Haiku 4.5 places best in coding, math, long context and lowest in multimodal, reasoning, instruction following.

### What is Claude Haiku 4.5 best at?

Its best category is agentic & tool use, where it ranks 52nd on Noometry.

### Cite this page

Noometry. (2026). Claude Haiku 4.5 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/claude-haiku-4-5

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