DeepSeek R1 0528 vs Llama 3.2 1B Instruct
DeepSeek R1 0528 (2025) and Llama 3.2 1B Instruct (2024) are frontier reasoning models from DeepSeek and AI at Meta. DeepSeek R1 0528 ships a 130k-token context window, while Llama 3.2 1B Instruct ships a 128k-token context window. On MMLU PRO, DeepSeek R1 0528 leads by 65 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 ~1752% cheaper at $0.03/1M; pay for DeepSeek R1 0528 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 0528 | Llama 3.2 1B Instruct |
|---|---|---|
| Best for | reasoning-heavy apps and provider-routed production | provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Long context |
| Context window | 130k | 128k |
| Cheapest output | $2.15/1M tokens | $0.20/1M tokens |
| Provider routes | 6 tracked | 7 tracked |
| Shared benchmarks | MMLU PRO leader | 2 rows |
Decision tradeoffs
- DeepSeek R1 0528 holds a shared-benchmark lead on MMLU PRO, ahead by 65 points.
- DeepSeek R1 0528 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- DeepSeek R1 0528 uniquely exposes Reasoning and Code execution in local model data.
- Local decision data tags DeepSeek R1 0528 for Coding, RAG, and Agents.
- Llama 3.2 1B Instruct has the lower cheapest tracked output price at $0.20/1M tokens.
- Llama 3.2 1B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- 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 R1 0528
$938
Cheapest tracked route/tier: OpenRouter
Llama 3.2 1B Instruct
$71.85
Cheapest tracked route/tier: Cloudflare Workers AI
Estimated monthly gap: $866. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Fireworks AI; start route-level A/B tests there.
- Llama 3.2 1B Instruct is $1.95/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning and Code execution before moving production traffic.
- Provider overlap exists on Fireworks AI and OpenRouter; start route-level A/B tests there.
- DeepSeek R1 0528 is $1.95/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- DeepSeek R1 0528 adds Reasoning and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-05-28 | 2024-09-25 |
| Context window | 130k | 128k |
| Parameters | 685B total, 37B active (MoE) | 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 R1 0528 | Llama 3.2 1B Instruct |
|---|---|---|
| Input price | $0.50/1M tokens | $0.03/1M tokens |
| Output price | $2.15/1M tokens | $0.20/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek R1 0528 | Llama 3.2 1B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | 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 R1 0528 | Llama 3.2 1B Instruct |
|---|---|---|
| MMLU PRO | 85.0 | 20.0 |
| Google-Proof Q&A | 81.0 | 25.6 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek R1 0528 at 85 and Llama 3.2 1B Instruct at 20, with DeepSeek R1 0528 ahead by 65 points; Google-Proof Q&A has DeepSeek R1 0528 at 81 and Llama 3.2 1B Instruct at 25.6, with DeepSeek R1 0528 ahead by 55.4 points. The largest visible gap is 65 points on MMLU PRO, 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 0528 and code execution: DeepSeek R1 0528. 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 R1 0528 lists $0.50/1M input and $2.15/1M 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 $0.92 per million blended tokens. Availability is 6 providers versus 7, so concentration risk also matters.
Choose DeepSeek R1 0528 when coding workflow support and larger context windows are central to the workload. Choose Llama 3.2 1B Instruct when provider fit, lower input-token cost, and broader provider choice 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 0528 or Llama 3.2 1B Instruct?
DeepSeek R1 0528 supports 130k 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 R1 0528 or Llama 3.2 1B Instruct?
Llama 3.2 1B Instruct is cheaper on tracked token pricing. DeepSeek R1 0528 costs $0.50/1M input and $2.15/1M 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 R1 0528 or Llama 3.2 1B Instruct open source?
DeepSeek R1 0528 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 reasoning mode, DeepSeek R1 0528 or Llama 3.2 1B Instruct?
DeepSeek R1 0528 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 0528 or Llama 3.2 1B Instruct?
Both DeepSeek R1 0528 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.
Where can I run DeepSeek R1 0528 and Llama 3.2 1B Instruct?
DeepSeek R1 0528 is available on Together AI, Fireworks AI, GCP Vertex AI, Novita AI, and OpenRouter. 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.