Anthropic, proprietary

# Claude Sonnet 4.5

> Claude Sonnet 4.5 by Anthropic, released September 2025. Ranked #81 of 354 with a Noometry Index of 44.1. API: $3 in / $15 out per M tokens. 200K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/claude-sonnet-4-5
- Last updated: 2026-10-10
- Title: Claude Sonnet 4.5 Benchmarks, Price & Rank (October 2026)

Claude Sonnet 4.5 by Anthropic ranks 81st of 354 ranked models on the Noometry Index as of October 2026, with a score of 44.1. Its strongest category is agentic & tool use, where it ranks 32nd. API pricing starts at $3 per million input tokens and $15 per million output tokens, with a 200K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #81 of 354
- **Index score:** 44.1
- **Evidence:** Confirmed 73 results
- **Provider:** [Anthropic](https://noometry.com/providers/anthropic)
- **Released:** September 29, 2025
- **Weights:** Proprietary
- **Reasoning:** Yes
- **Context window:** 200K
- **Max output:** 64K
- **Input price:** $3 / M
- **Output price:** $15 / M
- **Blended price:** $6 / M
- **Output speed:** 85 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #196 of 219
- **Knowledge cutoff:** July 2025
- **Input:** text, image, pdf

## Category scores

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

Claude Sonnet 4.5 category scores

1.  Coding 47.3
2.  Agentic & Tool Use 38.3
3.  Reasoning 26.9
4.  Math 32.3
5.  Knowledge 48.4
6.  Multimodal 34.8
7.  Multilingual 53.4
8.  Instruction Following 75.0
9.  Long Context 45.2
10.  Writing & Preference 66.5
11.  020406080

Claude Sonnet 4.5 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 47.3 | #61 | 8 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 38.3 | #32 | 11 |
| [Reasoning](https://noometry.com/best/reasoning) | 26.9 | #125 | 13 |
| [Math](https://noometry.com/best/math) | 32.3 | #216 | 7 |
| [Knowledge](https://noometry.com/best/knowledge) | 48.4 | #76 | 7 |
| [Multimodal](https://noometry.com/best/multimodal) | 34.8 | #89 | 1 |
| [Multilingual](https://noometry.com/best/multilingual) | 53.4 | #69 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 75.0 | #78 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 45.2 | #46 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 66.5 | #34 | 5 |

## Strengths and weaknesses

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

### Strongest categories

Claude Sonnet 4.5: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Writing & Preference](https://noometry.com/best/writing) | 66.5 | +12.8 | #34 of 312, top 11% |
| [Long Context](https://noometry.com/best/long-context) | 45.2 | +4.3 | #46 of 296, top 16% |
| [Coding](https://noometry.com/best/coding) | 47.3 | +8.6 | #61 of 340, top 18% |

### Weakest categories

Claude Sonnet 4.5: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multimodal](https://noometry.com/best/multimodal) | 34.8 | −3.8 | #89 of 128, top 70% |
| [Math](https://noometry.com/best/math) | 32.3 | −4.3 | #216 of 327, top 67% |
| [Reasoning](https://noometry.com/best/reasoning) | 26.9 | +3.3 | #125 of 350, top 36% |

## Closest competitors

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

Models ranked closest to Claude Sonnet 4.5
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Amazon Nova Experimental Chat 26 02 10](https://noometry.com/models/amazon-nova-experimental-chat-26-02-10) | #77 | 44.5 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-26-02-10-vs-claude-sonnet-4-5) |
| [DeepSeek-V3.2-Exp](https://noometry.com/models/deepseek-v3-2-exp) | #78 | 44.3 | $0.29 | 16 | [Compare](https://noometry.com/compare/claude-sonnet-4-5-vs-deepseek-v3-2-exp) |
| [Hy3](https://noometry.com/models/hy3) | #79 | 44.2 | $0.14 | — | [Compare](https://noometry.com/compare/claude-sonnet-4-5-vs-hy3) |
| [Inkling](https://noometry.com/models/inkling) | #80 | 44.1 | $2.57 | — | [Compare](https://noometry.com/compare/claude-sonnet-4-5-vs-inkling) |
| [Chatgpt 4o Latest 20250326](https://noometry.com/models/chatgpt-4o) | #82 | 43.8 | — | 21 | [Compare](https://noometry.com/compare/chatgpt-4o-vs-claude-sonnet-4-5) |
| [ERNIE 5.1](https://noometry.com/models/ernie-5-1) | #83 | 43.8 | — | — | [Compare](https://noometry.com/compare/claude-sonnet-4-5-vs-ernie-5-1) |
| [GLM-5V-Turbo](https://noometry.com/models/glm-5v-turbo) | #84 | 43.8 | $1.90 | — | [Compare](https://noometry.com/compare/claude-sonnet-4-5-vs-glm-5v-turbo) |
| [MiniMax-M3](https://noometry.com/models/minimax-m3) | #85 | 43.8 | $0.52 | — | [Compare](https://noometry.com/compare/claude-sonnet-4-5-vs-minimax-m3) |

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 Sonnet 4.5 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 71.3% | #23 of 32, top 72% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-05 |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 71.4% | #9 of 39, top 24% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1385 |  |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1393 | #76 of 113, top 68% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SWE-bench Multilingual](https://noometry.com/benchmarks/swe-bench-multilingual) | 67% | #8 of 13, top 62% |  | [SWE-bench](https://www.swebench.com/) | 2026-02-13 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 44.7% | #61 of 121, top 51% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 14.7% | #15 of 31, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 46.7% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 47.7% | #53 of 119, top 45% | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1485 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1489 | #31 of 294, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 796.15 | #59 of 105, top 57% | 32K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [AlgoTune](https://noometry.com/benchmarks/algotune) | 1.52 | #9 of 18, top 50% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

Claude Sonnet 4.5 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 46.5% | #19 of 41, top 47% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 73.2% | #2 of 49, top 5% | fc | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [GDPval](https://noometry.com/benchmarks/gdpval) | 42.5% | #4 of 11, top 37% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index) | 2.1% | #11 of 14, top 79% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Airline](https://noometry.com/benchmarks/tau2-airline) | 72% | #7 of 7, top 100% | enabled | [τ²-bench](https://taubench.com/) | 2026-02-26 |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 25.3% | #17 of 26, top 66% | enabled | [τ²-bench](https://taubench.com/) | 2026-02-26 |
| [τ²-bench Retail](https://noometry.com/benchmarks/tau2-retail) | 72.4% | #7 of 7, top 100% | enabled | [τ²-bench](https://taubench.com/) | 2026-02-26 |
| [τ²-bench Telecom](https://noometry.com/benchmarks/tau2-telecom) | 84.9% | #7 of 7, top 100% | enabled | [τ²-bench](https://taubench.com/) | 2026-02-26 |
| [Cybench](https://noometry.com/benchmarks/cybench) | 60% | #3 of 21, top 15% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 52.6% | #5 of 24, top 21% | 2K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepResearch Bench](https://noometry.com/benchmarks/deepresearch-bench) | 47.5% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [OSWorld](https://noometry.com/benchmarks/osworld) | 62.9% | #4 of 8, top 50% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Search](https://noometry.com/benchmarks/arena-search) | 1159 | #23 of 32, top 72% |  | [LMArena](https://lmarena.ai/leaderboard/search) | 2026-08-24 |
| [METR Time Horizons](https://noometry.com/benchmarks/metr-time-horizons) | 67.4% | #11 of 32, top 35% | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 3,839 | #37 of 60, top 62% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

Claude Sonnet 4.5 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 3.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 6.9% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 5.8% |  | 1K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 13.6% | #46 of 83, top 56% | 32K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 6.9% |  | 8K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 54.3% | #35 of 77, top 46% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 54.3% |  | 12K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 57.9% | #44 of 99, top 45% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 37.3% | #69 of 91, top 76% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 35.8% |  | no reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 25.5% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 48.3% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 31% |  | 1K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 63.7% | #47 of 83, top 57% | 32K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 46.5% |  | 8K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 1.1% | #78 of 134, top 59% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 4% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-06 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 12% | #76 of 129, top 59% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-08 |
| [EnigmaEval](https://noometry.com/benchmarks/enigmaeval) | 6% | #20 of 38, top 53% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 2.4% | #23 of 24, top 96% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-25 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1462 | #45 of 297, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1462 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 17% | #50 of 74, top 68% | 48K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-25 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 83.2% | #59 of 151, top 40% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 38.8% | #48 of 125, top 39% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 146.84 | #68 of 213, top 32% |  | [Epoch AI](https://epoch.ai/eci) | 2025-09-29 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 61.9 | #4 of 72, top 6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

Claude Sonnet 4.5 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 23.9% | #66 of 81, top 82% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 2.4% | #58 of 63, top 93% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-11 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 35.6% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-09-29 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 71.1% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-28 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 77.8% | #82 of 173, top 48% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-21 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 77.8% |  | 59K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-28 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 19% | #50 of 77, top 65% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 55.3% | #14 of 57, top 25% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1422 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1449 | #63 of 285, top 23% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [MATH Level 5](https://noometry.com/benchmarks/math-level-5) | 97.7% | #5 of 79, top 7% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-21 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 9.3% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-16 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 15.2% | #34 of 68, top 50% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-16 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 13.5% |  | 59K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-11-13 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 2.1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-09-29 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 4.2% | #30 of 55, top 55% | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-22 |

### Knowledge

Claude Sonnet 4.5 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 73.7% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-09-29 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 78.8% |  | 16K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-28 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 81.7% |  | 32K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-21 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 82.3% | #74 of 186, top 40% | 59K | [Epoch AI](https://epoch.ai/benchmarks) | 2025-10-28 |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 13.7% | #21 of 41, top 52% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 23.7% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 30.7% | #57 of 77, top 75% | 59K | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 86.9% | #3 of 58, top 6% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 12% | #74 of 96, top 78% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 68.6% | #11 of 57, top 20% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1472 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1482 | #40 of 273, top 15% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

Claude Sonnet 4.5 Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [VPCT](https://noometry.com/benchmarks/vpct) | 38% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [VPCT](https://noometry.com/benchmarks/vpct) | 39.8% | #14 of 24, top 59% | 32K | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Document](https://noometry.com/benchmarks/arena-document) | 1450 | #23 of 38, top 61% |  | [LMArena](https://lmarena.ai/leaderboard/document) | 2026-09-13 |

### Multilingual

Claude Sonnet 4.5 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1418 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1425 | #69 of 297, top 24% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1459 | #93 of 285, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1459 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1458 | #54 of 223, top 25% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1451 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1427 | #69 of 231, top 30% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1421 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1390 | #69 of 211, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1373 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1367 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1403 | #51 of 213, top 24% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1429 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1437 | #54 of 283, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1457 | #39 of 226, top 18% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1444 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

Claude Sonnet 4.5 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 85% | #19 of 57, top 34% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1459 | #35 of 298, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1458 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

Claude Sonnet 4.5 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1475 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1476 | #28 of 291, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

Claude Sonnet 4.5 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1439 | #66 of 297, top 23% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1435 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1425 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1442 | #35 of 295, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1678 | #34 of 115, top 30% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 85.4% | #9 of 57, top 16% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1465 | #35 of 295, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1454 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

Claude Sonnet 4.5 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [anthropic](https://docs.anthropic.com/en/docs/about-claude/models) | $3 | $15 | $0.30 | 2026-10-10 |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $3 | $15 | $0.30 | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $3 | $15 | $0.30 | 2026-10-10 |
| [openrouter](https://openrouter.ai/anthropic/claude-sonnet-4.5) | $3 | $15 | $0.30 | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude) | $3 | $15 | $0.30 | 2026-10-10 |

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

## Compare Claude Sonnet 4.5

-   [Claude Sonnet 4.5 vs Claude Sonnet 4](https://noometry.com/compare/claude-sonnet-4-vs-claude-sonnet-4-5)
-   [Claude Sonnet 4.5 vs Inkling](https://noometry.com/compare/claude-sonnet-4-5-vs-inkling)
-   [Claude Sonnet 4.5 vs Chatgpt 4o Latest 20250326](https://noometry.com/compare/chatgpt-4o-vs-claude-sonnet-4-5)
-   [Claude Sonnet 4.5 vs Hy3](https://noometry.com/compare/claude-sonnet-4-5-vs-hy3)
-   [Claude Sonnet 4.5 vs ERNIE 5.1](https://noometry.com/compare/claude-sonnet-4-5-vs-ernie-5-1)
-   [Claude Sonnet 4.5 vs DeepSeek-V3.2-Exp](https://noometry.com/compare/claude-sonnet-4-5-vs-deepseek-v3-2-exp)
-   [Claude Sonnet 4.5 vs GLM-5V-Turbo](https://noometry.com/compare/claude-sonnet-4-5-vs-glm-5v-turbo)
-   [Claude Sonnet 4.5 vs GPT-6 Astra](https://noometry.com/compare/claude-sonnet-4-5-vs-gpt-6-astra)
-   [Claude Sonnet 4.5 vs Gemini 3.8 Flash](https://noometry.com/compare/claude-sonnet-4-5-vs-gemini-3-8-flash)
-   [Claude Sonnet 4.5 vs Kimi K3](https://noometry.com/compare/claude-sonnet-4-5-vs-kimi-k3)
-   [Claude Sonnet 4.5 vs Grok 4.6](https://noometry.com/compare/claude-sonnet-4-5-vs-grok-4-6)
-   [Claude Sonnet 4.5 vs Qwen3.8 Max](https://noometry.com/compare/claude-sonnet-4-5-vs-qwen3-8-max)
-   [Claude Sonnet 4.5 vs GLM-5.3](https://noometry.com/compare/claude-sonnet-4-5-vs-glm-5-3)
-   [Claude Sonnet 4.5 vs Muse Spark 1.3](https://noometry.com/compare/claude-sonnet-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 Sonnet 4.5?

Claude Sonnet 4.5 by Anthropic ranks 81st of 354 ranked models on the Noometry Index as of October 2026, with a score of 44.1. Its strongest category is agentic & tool use, where it ranks 32nd. API pricing starts at $3 per million input tokens and $15 per million output tokens, with a 200K-token context window.

### How much does Claude Sonnet 4.5 cost?

Claude Sonnet 4.5 costs $3 per million input tokens and $15 per million output tokens on Anthropic's own API, with cached input at $0.30.

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

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

### Is Claude Sonnet 4.5 open source?

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

### How fast is Claude Sonnet 4.5?

Claude Sonnet 4.5 generated about 85 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

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

Relative to other ranked models, Claude Sonnet 4.5 places best in writing & preference, long context, coding and lowest in multimodal, math, reasoning.

### What is Claude Sonnet 4.5 best at?

Its best category is agentic & tool use, where it ranks 32nd on Noometry.

### Cite this page

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

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/claude-sonnet-4-5.md).