DeepSeek V3.2 vs Llama 3.2 1B
DeepSeek V3.2 (2025) and Llama 3.2 1B (2024) are compact production models from DeepSeek and AI at Meta. DeepSeek V3.2 ships a 160K-token context window, while Llama 3.2 1B ships a 128K-token context window. On pricing, Llama 3.2 1B costs $0.1/1M input tokens versus $0.26/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3.2 1B is ~159% cheaper at $0.1/1M; pay for DeepSeek V3.2 only for coding workflow support.
Specs
| Released | 2025-01-01 | 2024-09-25 |
| Context window | 160K | 128K |
| Parameters | 671B | 1.23B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| DeepSeek V3.2 | Llama 3.2 1B | |
|---|---|---|
| Input price | $0.26/1M tokens | $0.1/1M tokens |
| Output price | $0.42/1M tokens | $0.1/1M tokens |
| Providers |
Capabilities
| DeepSeek V3.2 | Llama 3.2 1B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on structured outputs: DeepSeek V3.2 and code execution: DeepSeek V3.2. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, DeepSeek V3.2 lists $0.26/1M input and $0.42/1M output tokens, while Llama 3.2 1B lists $0.1/1M input and $0.1/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B lower by about $0.21 per million blended tokens. Availability is 4 providers versus 1, so concentration risk also matters.
Choose DeepSeek V3.2 when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Llama 3.2 1B when provider fit and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, DeepSeek V3.2 or Llama 3.2 1B?
DeepSeek V3.2 supports 160K tokens, while Llama 3.2 1B supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, DeepSeek V3.2 or Llama 3.2 1B?
Llama 3.2 1B is cheaper on tracked token pricing. DeepSeek V3.2 costs $0.26/1M input and $0.42/1M output tokens. Llama 3.2 1B costs $0.1/1M input and $0.1/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.2 or Llama 3.2 1B open source?
DeepSeek V3.2 is listed under Open Source. Llama 3.2 1B is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for structured outputs, DeepSeek V3.2 or Llama 3.2 1B?
DeepSeek V3.2 has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for code execution, DeepSeek V3.2 or Llama 3.2 1B?
DeepSeek V3.2 has the clearer documented code execution signal in this comparison. If code execution is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek V3.2 and Llama 3.2 1B?
DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, and OpenRouter. Llama 3.2 1B is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.