DeepSeek-V3.2 7B: pricing, context window and benchmarks
DeepSeek-V3.2 7B is a small open-weight model from DeepSeek. It offers a 4,096-token context window, costs $1.75 per million input tokens and $6.84 per million output tokens, and averages 63.2 across the index's benchmark suite.
Pricing
DeepSeek-V3.2 7B costs $1.75 per million input tokens and $6.84 per million output tokens. A blended 3:1 input-to-output mix works out to about $3.02 per million tokens. Cached input is listed at $0.19 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $1.75 | per 1M tokens |
| Output | $6.84 | per 1M tokens |
| Cached input | $0.19 | per 1M tokens |
| Blended 3:1 | $3.02 | estimate per 1M tokens |
| 1M in + 1M out | $8.59 | estimate |
Benchmarks
Scores below are modelled estimates on a 0–100 scale. Use them to compare relative position, not as official vendor results.
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 68.2 | 414 | 31th | |
| GPQA Diamond | 60.2 | 403 | 33th | |
| SWE-bench Verified | 49.7 | 424 | 29th | |
| HumanEval | 67.1 | 420 | 30th | |
| MATH-500 | 65.8 | 415 | 31th | |
| AIME 2025 | 54.3 | 420 | 30th | |
| LiveCodeBench | 50.2 | 446 | 26th | |
| IFEval | 75.1 | 397 | 34th | |
| TruthfulQA | 67.6 | 420 | 30th | |
| HellaSwag | 75.8 | 428 | 29th | |
| MMMU | 61.3 | 417 | 31th | |
| Arena Elo | 1311.8 | 405 | 33th |
What DeepSeek-V3.2 7B is good for
DeepSeek-V3.2 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $1.75 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $1.75 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
- Accepts more than text, including image input where the provider supports it.
Trade-offs
- Context window of 4,096 tokens is small for long-document work.
- Average benchmark score is below the index median; test it on your own task.
- Less reliable on open-ended reasoning and complex multi-step instructions.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Llama 4 Maverick 8B | Meta AI | 4096 | $1.64 | 63.4 |
| Fusion Mini | OpenRouter | 4096 | $4.89 | 63.3 |
| DeepSeek-V3.2 7B | DeepSeek | 4096 | $1.75 | 63.2 |
| Firefunction Mini | Fireworks AI | 32768 | $4.63 | 63.2 |
| GLM-4.6 8B | Zhipu AI | 8192 | $1.69 | 63.1 |
| SmolLM3 | Hugging Face | 8192 | $1.71 | 63.1 |
FAQ
How much does DeepSeek-V3.2 7B cost?
DeepSeek-V3.2 7B is listed at $1.75 per million input tokens and $6.84 per million output tokens. Cached input is $0.19 per million tokens. A blended 3:1 input-to-output mix is about $3.02 per million tokens.
What is the context window of DeepSeek-V3.2 7B?
DeepSeek-V3.2 7B has a context window of 4,096 tokens and can emit up to 4,096 output tokens per request in this index.
Is DeepSeek-V3.2 7B open weights?
Yes. DeepSeek-V3.2 7B is listed as an open-weight model under a open weights licence.
How does DeepSeek-V3.2 7B score on benchmarks?
DeepSeek-V3.2 7B averages 63.2 across the scored benchmarks in this index, ranking #416 of 600 models. Its strongest relative result is Arena Elo at 1311.8.
What is DeepSeek-V3.2 7B best for?
DeepSeek-V3.2 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $1.75 per million input tokens it can handle a large share of production traffic.