DeepSeek R1 vs Llama 3.2 1B
DeepSeek R1 (2025) and Llama 3.2 1B (2024) are frontier reasoning models from DeepSeek and AI at Meta. DeepSeek R1 ships a 128k-token context window, while Llama 3.2 1B ships a 128k-token context window. On HumanEval, DeepSeek R1 leads by 61.8 pts. On pricing, both list $0.10/1M input tokens on the cheapest tracked route. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Pick DeepSeek R1 for coding; Llama 3.2 1B is better when provider fit matters more.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 | Llama 3.2 1B |
|---|---|---|
| Best for | reasoning-heavy apps and provider-routed production | general production evaluation |
| Decision fit | Coding, RAG, and Agents | Coding, Long context, and Classification |
| Context window | 128k | 128k |
| Cheapest output | $0.30/1M tokens | $0.10/1M tokens |
| Provider routes | 14 tracked | 1 tracked |
| Shared benchmarks | HumanEval leader | 1 shared |
Decision tradeoffs
- DeepSeek R1 holds a shared-benchmark lead on HumanEval, ahead by 61.8 points.
- DeepSeek R1 has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek R1 uniquely exposes Reasoning, Structured outputs, and Code execution in local model data.
- Local decision data tags DeepSeek R1 for Coding, RAG, and Agents.
- Llama 3.2 1B has the lower cheapest tracked output price at $0.10/1M tokens.
- Local decision data tags Llama 3.2 1B for Coding, Long context, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek R1
$155
Cheapest tracked route/tier: Bitdeer AI
Llama 3.2 1B
$105
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $50.00. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- Llama 3.2 1B is $0.20/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Structured outputs, and Code execution before moving production traffic.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- DeepSeek R1 is $0.20/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- DeepSeek R1 adds Reasoning, Structured outputs, and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-20 | 2024-09-25 |
| Context window | 128k | 128k |
| Parameters | 671B, 37B Active | 1.23B |
| Architecture | Decoder Only | Decoder Only |
| License | MITOSI-approved | Llama 3 Community |
| Openness | Open source | Open weights |
| Weights | Available | Unknown |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| Knowledge cutoff | 2023-12 | 2023-12 |
Pricing and availability
| Pricing attribute | DeepSeek R1 | Llama 3.2 1B |
|---|---|---|
| Input price | $0.10/1M tokens | $0.10/1M tokens |
| Output price | $0.30/1M tokens | $0.10/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek R1 | Llama 3.2 1B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek R1 | Llama 3.2 1B |
|---|---|---|
| HumanEval | 89.9 | 28.1 |
Deep dive
On shared benchmark coverage, HumanEval has DeepSeek R1 at 89.9 and Llama 3.2 1B at 28.1, with DeepSeek R1 ahead by 61.8 points. The largest visible gap is 61.8 points on HumanEval, 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 reasoning mode: DeepSeek R1, structured outputs: DeepSeek R1, and code execution: DeepSeek R1. 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 R1 lists $0.10/1M input and $0.30/1M output tokens on the cheapest tracked provider, while Llama 3.2 1B lists $0.10/1M input and $0.10/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.2 1B lower by about $0.06 per million blended tokens. Availability is 14 providers versus 1, so concentration risk also matters.
Choose DeepSeek R1 when coding workflow support and broader provider choice are central to the workload. Choose Llama 3.2 1B when provider fit 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 R1 or Llama 3.2 1B?
DeepSeek R1 supports 128k 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 R1 or Llama 3.2 1B?
Llama 3.2 1B is cheaper on tracked token pricing. DeepSeek R1 costs $0.10/1M input and $0.30/1M output tokens. Llama 3.2 1B costs $0.10/1M input and $0.10/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek R1 or Llama 3.2 1B open source?
DeepSeek R1 is listed under MIT. Llama 3.2 1B 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 reasoning mode, DeepSeek R1 or Llama 3.2 1B?
DeepSeek R1 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, DeepSeek R1 or Llama 3.2 1B?
DeepSeek R1 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.
Where can I run DeepSeek R1 and Llama 3.2 1B?
DeepSeek R1 is available on DeepSeek Platform, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Llama 3.2 1B is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-07-09. Data sourced from public model cards and provider documentation.