DeepSeek, open weights

# DeepSeek-V3.2-Exp

> DeepSeek-V3.2-Exp by DeepSeek, released September 2025. Ranked #78 of 354 with a Noometry Index of 44.3. API: $0.26 in / $0.38 out per M tokens. 164K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/deepseek-v3-2-exp
- Last updated: 2026-10-10
- Title: DeepSeek-V3.2-Exp Benchmarks, Price & Rank (October 2026)

DeepSeek-V3.2-Exp by DeepSeek ranks 78th of 354 ranked models on the Noometry Index as of October 2026, with a score of 44.3. Its strongest category is long context, where it ranks 16th. API pricing starts at $0.26 per million input tokens and $0.38 per million output tokens, with a 164K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #78 of 354
- **Index score:** 44.3
- **Evidence:** Confirmed 49 results
- **Provider:** [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek)
- **Released:** September 29, 2025
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 164K
- **Max output:** 66K
- **Input price:** $0.26 / M
- **Output price:** $0.38 / M
- **Blended price:** $0.29 / M
- **Output speed:** 16 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #49 of 219
- **Knowledge cutoff:** December 2024
- **Input:** text
- **Hugging Face:** [deepseek-ai/DeepSeek-V3.2-Exp](https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp)

## Category scores

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

DeepSeek-V3.2-Exp category scores

1.  Coding 46.5
2.  Agentic & Tool Use 32.7
3.  Reasoning 22.1
4.  Math 41.7
5.  Knowledge 51.7
6.  Multilingual 52.2
7.  Instruction Following 74.5
8.  Long Context 47.6
9.  Writing & Preference 62.4
10.  020406080

DeepSeek-V3.2-Exp category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 46.5 | #65 | 7 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 32.7 | #59 | 4 |
| [Reasoning](https://noometry.com/best/reasoning) | 22.1 | #208 | 10 |
| [Math](https://noometry.com/best/math) | 41.7 | #87 | 4 |
| [Knowledge](https://noometry.com/best/knowledge) | 51.7 | #66 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 52.2 | #90 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 74.5 | #93 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 47.6 | #16 | 4 |
| [Writing & Preference](https://noometry.com/best/writing) | 62.4 | #77 | 4 |

## Strengths and weaknesses

Categories where DeepSeek-V3.2-Exp places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

DeepSeek-V3.2-Exp: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 47.6 | +6.6 | #16 of 296, top 6% |
| [Coding](https://noometry.com/best/coding) | 46.5 | +7.8 | #65 of 340, top 20% |
| [Knowledge](https://noometry.com/best/knowledge) | 51.7 | +14.3 | #66 of 314, top 22% |

### Weakest categories

DeepSeek-V3.2-Exp: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Reasoning](https://noometry.com/best/reasoning) | 22.1 | −1.5 | #208 of 350, top 60% |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 32.7 | +2.4 | #59 of 154, top 39% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 74.5 | +3.2 | #93 of 305, top 31% |

## Closest competitors

The models ranked just above and below DeepSeek-V3.2-Exp. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to DeepSeek-V3.2-Exp
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [MiMo-V2.5-Pro](https://noometry.com/models/mimo-v2-5-pro) | #74 | 45.2 | $0.54 | — | [Compare](https://noometry.com/compare/deepseek-v3-2-exp-vs-mimo-v2-5-pro) |
| [Gemini 2.5 Pro](https://noometry.com/models/gemini-2-5-pro) | #75 | 45.0 | $3.44 | 5 | [Compare](https://noometry.com/compare/deepseek-v3-2-exp-vs-gemini-2-5-pro) |
| [GPT-5.4 mini](https://noometry.com/models/gpt-5-4-mini) | #76 | 45.0 | $1.69 | 10 | [Compare](https://noometry.com/compare/deepseek-v3-2-exp-vs-gpt-5-4-mini) |
| [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-deepseek-v3-2-exp) |
| [Hy3](https://noometry.com/models/hy3) | #79 | 44.2 | $0.14 | — | [Compare](https://noometry.com/compare/deepseek-v3-2-exp-vs-hy3) |
| [Inkling](https://noometry.com/models/inkling) | #80 | 44.1 | $2.57 | — | [Compare](https://noometry.com/compare/deepseek-v3-2-exp-vs-inkling) |
| [Claude Sonnet 4.5](https://noometry.com/models/claude-sonnet-4-5) | #81 | 44.1 | $6 | 85 | [Compare](https://noometry.com/compare/claude-sonnet-4-5-vs-deepseek-v3-2-exp) |
| [Chatgpt 4o Latest 20250326](https://noometry.com/models/chatgpt-4o) | #82 | 43.8 | — | 21 | [Compare](https://noometry.com/compare/chatgpt-4o-vs-deepseek-v3-2-exp) |

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

DeepSeek-V3.2-Exp Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 70% | #11 of 39, top 29% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 70.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 74.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 70.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Aider Polyglot](https://noometry.com/benchmarks/aider-polyglot) | 74.2% | #6 of 44, top 14% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1272 |  |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1362 | #83 of 113, top 74% | thinking | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SWE-bench Multilingual](https://noometry.com/benchmarks/swe-bench-multilingual) | 59% | #12 of 13, top 93% |  | [SWE-bench](https://www.swebench.com/) | 2026-02-13 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 38.9% | #85 of 121, top 71% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 39.5% | #79 of 119, top 67% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1454 | #80 of 294, top 28% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1439 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Agentic & Tool Use

DeepSeek-V3.2-Exp Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 39.6% | #24 of 41, top 59% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 21.3% | #48 of 49, top 98% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl) | 56.7% | #11 of 49, top 23% | prompt + thinking | [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) |  |
| [TheAgentCompany](https://noometry.com/benchmarks/the-agent-company) | 42.9% | Best of 14 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 1,034 | #47 of 60, top 79% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

DeepSeek-V3.2-Exp Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 4% | #62 of 83, top 75% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 52.2% | #60 of 99, top 61% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 36.7% | #70 of 91, top 77% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 57% | #53 of 83, top 64% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 2.9% | #68 of 134, top 51% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 14% | #69 of 129, top 54% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-16 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 1% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-16 |
| [Thematic Generalization](https://noometry.com/benchmarks/thematic-generalization) | 65% | #9 of 23, top 40% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/generalization) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1434 | #86 of 297, top 29% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1429 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 62.7% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 85.6% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 87.7% | #47 of 151, top 32% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 28.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 15.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 29.1% | #78 of 125, top 63% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 145 |  |  | [Epoch AI](https://epoch.ai/eci) | 2025-09-29 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 146.27 | #74 of 213, top 35% |  | [Epoch AI](https://epoch.ai/eci) | 2025-12-01 |

### Math

DeepSeek-V3.2-Exp Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 57.7% | #24 of 29, top 83% | think | [MathArena](https://matharena.ai/) |  |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 48.9% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-16 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 87.8% | #59 of 173, top 35% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-16 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 8% | #66 of 77, top 86% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1435 | #78 of 285, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1423 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 22.1% | #27 of 68, top 40% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-22 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 2.1% | #37 of 55, top 68% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-16 |

### Knowledge

DeepSeek-V3.2-Exp Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 83.4% | #69 of 186, top 38% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2025-12-16 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 71.2% |  |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-07-16 |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 6.3% |  |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 5.3% | #13 of 96, top 14% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1436 | #89 of 273, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1422 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

DeepSeek-V3.2-Exp Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1409 | #90 of 297, top 31% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1404 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1456 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1461 | #89 of 285, top 32% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1433 | #87 of 223, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1429 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1440 | #57 of 231, top 25% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1413 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1331 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1374 | #85 of 211, top 41% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1371 | #85 of 213, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1370 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1424 | #75 of 283, top 27% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1411 |  | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1440 | #66 of 226, top 30% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1423 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

DeepSeek-V3.2-Exp Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1413 | #83 of 298, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1401 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

DeepSeek-V3.2-Exp Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 52.8% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 83.3% | #9 of 47, top 20% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 12.4% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 13.2% | #18 of 19, top 95% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CL-bench Life](https://noometry.com/benchmarks/cl-bench-life) | 7.4% |  |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CL-bench Life](https://noometry.com/benchmarks/cl-bench-life) | 9.5% | #11 of 13, top 85% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1428 | #79 of 291, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1418 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

DeepSeek-V3.2-Exp Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1425 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1425 | #87 of 297, top 30% | thinking | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1403 | #71 of 295, top 25% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1400 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1515 | #53 of 115, top 47% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1427 | #88 of 295, top 30% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1422 |  |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

DeepSeek-V3.2-Exp API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) | $0.58 | $1.68 | — | 2026-10-10 |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.62 | $1.85 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.26 | $0.38 | $0.13 | 2026-10-10 |
| [openrouter](https://openrouter.ai/deepseek/deepseek-v3.2-exp) | $0.27 | $0.41 | — | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/models) | $0.56 | $1.68 | $0.056 | 2026-10-10 |

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

## Compare DeepSeek-V3.2-Exp

-   [DeepSeek-V3.2-Exp vs Amazon Nova Experimental Chat 26 02 10](https://noometry.com/compare/amazon-nova-experimental-chat-26-02-10-vs-deepseek-v3-2-exp)
-   [DeepSeek-V3.2-Exp vs Hy3](https://noometry.com/compare/deepseek-v3-2-exp-vs-hy3)
-   [DeepSeek-V3.2-Exp vs GPT-5.4 mini](https://noometry.com/compare/deepseek-v3-2-exp-vs-gpt-5-4-mini)
-   [DeepSeek-V3.2-Exp vs Inkling](https://noometry.com/compare/deepseek-v3-2-exp-vs-inkling)
-   [DeepSeek-V3.2-Exp vs Gemini 2.5 Pro](https://noometry.com/compare/deepseek-v3-2-exp-vs-gemini-2-5-pro)
-   [DeepSeek-V3.2-Exp vs Claude Sonnet 4.5](https://noometry.com/compare/claude-sonnet-4-5-vs-deepseek-v3-2-exp)
-   [DeepSeek-V3.2-Exp vs GPT-6 Astra](https://noometry.com/compare/deepseek-v3-2-exp-vs-gpt-6-astra)
-   [DeepSeek-V3.2-Exp vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-deepseek-v3-2-exp)
-   [DeepSeek-V3.2-Exp vs Gemini 3.8 Flash](https://noometry.com/compare/deepseek-v3-2-exp-vs-gemini-3-8-flash)
-   [DeepSeek-V3.2-Exp vs Kimi K3](https://noometry.com/compare/deepseek-v3-2-exp-vs-kimi-k3)
-   [DeepSeek-V3.2-Exp vs Grok 4.6](https://noometry.com/compare/deepseek-v3-2-exp-vs-grok-4-6)
-   [DeepSeek-V3.2-Exp vs Qwen3.8 Max](https://noometry.com/compare/deepseek-v3-2-exp-vs-qwen3-8-max)
-   [DeepSeek-V3.2-Exp vs GLM-5.3](https://noometry.com/compare/deepseek-v3-2-exp-vs-glm-5-3)
-   [DeepSeek-V3.2-Exp vs Muse Spark 1.3](https://noometry.com/compare/deepseek-v3-2-exp-vs-muse-spark-1-3)

## Other DeepSeek models

-   [DeepSeek V4 Pro](https://noometry.com/models/deepseek-v4-pro)54.3
-   [DeepSeek V4 Flash](https://noometry.com/models/deepseek-v4-flash)53.6
-   [DeepSeek V4.1 Flash](https://noometry.com/models/deepseek-v4-1-flash)52.8
-   [DeepSeek-V3.1-Terminus](https://noometry.com/models/deepseek-v3-1-terminus)43.1
-   [DeepSeek-V3.1](https://noometry.com/models/deepseek-v3-1)42.8
-   [DeepSeek-R1](https://noometry.com/models/deepseek-r1)42.3
-   [DeepSeek-V3.2-Speciale](https://noometry.com/models/deepseek-v3-2-speciale)39.7
-   [DeepSeek-V3](https://noometry.com/models/deepseek-v3)39.5

## Frequently asked questions

### How good is DeepSeek-V3.2-Exp?

DeepSeek-V3.2-Exp by DeepSeek ranks 78th of 354 ranked models on the Noometry Index as of October 2026, with a score of 44.3. Its strongest category is long context, where it ranks 16th. API pricing starts at $0.26 per million input tokens and $0.38 per million output tokens, with a 164K-token context window.

### How much does DeepSeek-V3.2-Exp cost?

DeepSeek-V3.2-Exp costs $0.26 per million input tokens and $0.38 per million output tokens on deepinfra, with cached input at $0.13.

### What is DeepSeek-V3.2-Exp's context window?

DeepSeek-V3.2-Exp accepts up to 164K tokens of input and can write up to 66K tokens in one response.

### Is DeepSeek-V3.2-Exp open source?

Yes. DeepSeek-V3.2-Exp's weights are downloadable from Hugging Face (deepseek-ai/DeepSeek-V3.2-Exp); check the license for commercial terms.

### How fast is DeepSeek-V3.2-Exp?

DeepSeek-V3.2-Exp generated about 16 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are DeepSeek-V3.2-Exp's strengths and weaknesses?

Relative to other ranked models, DeepSeek-V3.2-Exp places best in long context, coding, knowledge and lowest in reasoning, agentic & tool use, instruction following.

### What is DeepSeek-V3.2-Exp best at?

Its best category is long context, where it ranks 16th on Noometry.

### Cite this page

Noometry. (2026). DeepSeek-V3.2-Exp benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/deepseek-v3-2-exp

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/deepseek-v3-2-exp.md).