DeepSeek V3.2 vs Llama 3.2 1B Instruct
DeepSeek V3.2 (2025) and Llama 3.2 1B Instruct (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 Instruct ships a 128k-token context window. On Google-Proof Q&A, DeepSeek V3.2 leads by 58.4 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3.2 1B Instruct is ~833% cheaper at $0.03/1M; pay for DeepSeek V3.2 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.2 | Llama 3.2 1B Instruct |
|---|---|---|
| Best for | provider-routed production | provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Long context |
| Context window | 160k | 128k |
| Cheapest output | $0.38/1M tokens | $0.20/1M tokens |
| Provider routes | 7 tracked | 7 tracked |
| Shared benchmarks | Google-Proof Q&A leader | 1 rows |
Decision tradeoffs
- DeepSeek V3.2 holds a shared-benchmark lead on Google-Proof Q&A, ahead by 58.4 points.
- DeepSeek V3.2 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- DeepSeek V3.2 uniquely exposes Code execution in local model data.
- Local decision data tags DeepSeek V3.2 for Coding, RAG, and Agents.
- Llama 3.2 1B Instruct has the lower cheapest tracked output price at $0.20/1M tokens.
- Local decision data tags Llama 3.2 1B Instruct for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek V3.2
$296
Cheapest tracked route/tier: OpenRouter
Llama 3.2 1B Instruct
$71.85
Cheapest tracked route/tier: Cloudflare Workers AI
Estimated monthly gap: $224. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, Fireworks AI, and NVIDIA NIM; start route-level A/B tests there.
- Llama 3.2 1B Instruct is $0.18/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Code execution before moving production traffic.
- Provider overlap exists on Fireworks AI, NVIDIA NIM, and AWS Bedrock; start route-level A/B tests there.
- DeepSeek V3.2 is $0.18/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- DeepSeek V3.2 adds Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-01 | 2024-09-25 |
| Context window | 160k | 128k |
| Parameters | 671B | 1.23B |
| Architecture | decoder only | decoder only |
| License | MIT(OSI) | Llama 3 Community |
| Openness | Open source | Open weights |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Pricing attribute | DeepSeek V3.2 | Llama 3.2 1B Instruct |
|---|---|---|
| Input price | $0.25/1M tokens | $0.03/1M tokens |
| Output price | $0.38/1M tokens | $0.20/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3.2 | Llama 3.2 1B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V3.2 | Llama 3.2 1B Instruct |
|---|---|---|
| Google-Proof Q&A | 84.0 | 25.6 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has DeepSeek V3.2 at 84 and Llama 3.2 1B Instruct at 25.6, with DeepSeek V3.2 ahead by 58.4 points. The largest visible gap is 58.4 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on code execution: DeepSeek V3.2. Both models share structured outputs, 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 $0.25/1M input and $0.38/1M output tokens on the cheapest tracked provider, while Llama 3.2 1B Instruct lists $0.03/1M input and $0.20/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B Instruct lower by about $0.21 per million blended tokens. Availability is 7 providers versus 7, so concentration risk also matters.
Choose DeepSeek V3.2 when coding workflow support and larger context windows are central to the workload. Choose Llama 3.2 1B Instruct 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.
FAQ
Which has a larger context window, DeepSeek V3.2 or Llama 3.2 1B Instruct?
DeepSeek V3.2 supports 160k tokens, while Llama 3.2 1B Instruct 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 Instruct?
Llama 3.2 1B Instruct is cheaper on tracked token pricing. DeepSeek V3.2 costs $0.25/1M input and $0.38/1M output tokens. Llama 3.2 1B Instruct costs $0.03/1M input and $0.20/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.2 or Llama 3.2 1B Instruct open source?
DeepSeek V3.2 is listed under MIT. Llama 3.2 1B Instruct is listed under Llama 3 Community. 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 Instruct?
Both DeepSeek V3.2 and Llama 3.2 1B Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for code execution, DeepSeek V3.2 or Llama 3.2 1B Instruct?
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 Instruct?
DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, OpenRouter, and Microsoft Foundry. Llama 3.2 1B Instruct is available on Cloudflare Workers AI, OpenRouter, Fireworks AI, NVIDIA NIM, and Bitdeer AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.