DeepSeek R1 vs Llama 3.3 70B
DeepSeek R1 (2025) and Llama 3.3 70B (2025) are frontier reasoning models from DeepSeek and AI at Meta. DeepSeek R1 ships a 128k-token context window, while Llama 3.3 70B ships a 8k-token context window. On pricing, DeepSeek R1 costs $0.10/1M input tokens versus $0.90/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
DeepSeek R1 is ~800% cheaper at $0.10/1M; pay for Llama 3.3 70B only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 | Llama 3.3 70B |
|---|---|---|
| Best for | reasoning-heavy apps and provider-routed production | multimodal apps and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | Agents, Vision, and Classification |
| Context window | 128k | 8k |
| Cheapest output | $0.30/1M tokens | $0.90/1M tokens |
| Provider routes | 14 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- DeepSeek R1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- DeepSeek R1 has the lower cheapest tracked output price at $0.30/1M tokens.
- 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 3.3 70B uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Llama 3.3 70B for Agents, Vision, 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 3.3 70B
$945
Cheapest tracked route/tier: Fireworks AI
Estimated monthly gap: $790. 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 3.3 70B is $0.60/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Reasoning, Structured outputs, and Code execution before moving production traffic.
- Llama 3.3 70B adds Vision, Multimodal, and Function calling in local capability data.
- Provider overlap exists on Fireworks AI; start route-level A/B tests there.
- DeepSeek R1 is $0.60/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
- DeepSeek R1 adds Reasoning, Structured outputs, and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-20 | 2025-12-09 |
| Context window | 128k | 8k |
| Parameters | 671B, 37B Active | 70B |
| 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 | 2024-12 |
Pricing and availability
| Pricing attribute | DeepSeek R1 | Llama 3.3 70B |
|---|---|---|
| Input price | $0.10/1M tokens | $0.90/1M tokens |
| Output price | $0.30/1M tokens | $0.90/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek R1 | Llama 3.3 70B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Llama 3.3 70B, multimodal input: Llama 3.3 70B, reasoning mode: DeepSeek R1, function calling: Llama 3.3 70B, tool use: Llama 3.3 70B, 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 3.3 70B lists $0.90/1M input and $0.90/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek R1 lower by about $0.74 per million blended tokens. Availability is 14 providers versus 1, so concentration risk also matters.
Choose DeepSeek R1 when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose Llama 3.3 70B when vision-heavy evaluation 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 3.3 70B?
DeepSeek R1 supports 128k tokens, while Llama 3.3 70B supports 8k 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 3.3 70B?
DeepSeek R1 is cheaper on tracked token pricing. DeepSeek R1 costs $0.10/1M input and $0.30/1M output tokens. Llama 3.3 70B costs $0.90/1M input and $0.90/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek R1 or Llama 3.3 70B open source?
DeepSeek R1 is listed under MIT. Llama 3.3 70B 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 vision, DeepSeek R1 or Llama 3.3 70B?
Llama 3.3 70B has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, DeepSeek R1 or Llama 3.3 70B?
Llama 3.3 70B has the clearer documented multimodal input signal in this comparison. If multimodal input 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 3.3 70B?
DeepSeek R1 is available on DeepSeek Platform, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Llama 3.3 70B is available on Fireworks AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.