DeepSeek V3.1 vs Llama 3 70B Instruct
DeepSeek V3.1 (2026) and Llama 3 70B Instruct (2024) are compact production models from DeepSeek and AI at Meta. DeepSeek V3.1 ships a 64K-token context window, while Llama 3 70B Instruct ships a 8K-token context window. On MMLU PRO, DeepSeek V3.1 leads by 25.9 pts. On pricing, Llama 3 70B Instruct costs $0.4/1M input tokens versus $0.56/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 3 70B Instruct is ~40% cheaper at $0.4/1M; pay for DeepSeek V3.1 only for coding workflow support.
Specs
| Released | 2026-03-01 | 2024-04-18 |
| Context window | 64K | 8K |
| Parameters | — | 70B |
| Architecture | mixture of experts | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| DeepSeek V3.1 | Llama 3 70B Instruct | |
|---|---|---|
| Input price | $0.56/1M tokens | $0.4/1M tokens |
| Output price | $1.68/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| DeepSeek V3.1 | Llama 3 70B Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | DeepSeek V3.1 | Llama 3 70B Instruct |
|---|---|---|
| MMLU PRO | 83.3 | 57.4 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V3.1 at 83.3 and Llama 3 70B Instruct at 57.4, with DeepSeek V3.1 ahead by 25.9 points. The largest visible gap is 25.9 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 vision: DeepSeek V3.1, multimodal input: DeepSeek V3.1, and code execution: DeepSeek V3.1. 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 V3.1 lists $0.56/1M input and $1.68/1M output tokens, while Llama 3 70B Instruct lists $0.4/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 70B Instruct lower by about $0.5 per million blended tokens. Availability is 6 providers versus 18, so concentration risk also matters.
Choose DeepSeek V3.1 when coding workflow support and larger context windows are central to the workload. Choose Llama 3 70B 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 V3.1 or Llama 3 70B Instruct?
DeepSeek V3.1 supports 64K tokens, while Llama 3 70B Instruct 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 V3.1 or Llama 3 70B Instruct?
Llama 3 70B Instruct is cheaper on tracked token pricing. DeepSeek V3.1 costs $0.56/1M input and $1.68/1M output tokens. Llama 3 70B Instruct costs $0.4/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.1 or Llama 3 70B Instruct open source?
DeepSeek V3.1 is listed under Open Source. Llama 3 70B Instruct 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 vision, DeepSeek V3.1 or Llama 3 70B Instruct?
DeepSeek V3.1 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, DeepSeek V3.1 or Llama 3 70B Instruct?
DeepSeek V3.1 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 V3.1 and Llama 3 70B Instruct?
DeepSeek V3.1 is available on Microsoft Foundry, Fireworks AI, NVIDIA NIM, Together AI, and AWS Bedrock. Llama 3 70B Instruct is available on GCP Vertex AI, AWS Bedrock, Microsoft Foundry, NVIDIA NIM, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.