DeepSeek V3.2 vs Llama 3.1 70B Instruct
DeepSeek V3.2 (2025) and Llama 3.1 70B Instruct (2024) are compact production models from DeepSeek and AI at Meta. DeepSeek V3.2 ships a 160K-token context window, while Llama 3.1 70B Instruct ships a 128K-token context window. On LMArena Text Arena - Creative Writing, DeepSeek V3.2 leads by 167 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
DeepSeek V3.2 is ~59% cheaper at $0.25/1M; pay for Llama 3.1 70B Instruct only for provider fit.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.2 | Llama 3.1 70B 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 | $0.40/1M tokens |
| Provider routes | 8 tracked | 14 tracked |
| Shared benchmarks | LMArena Text Arena - Creative Writing leader | 2 shared |
Decision tradeoffs
- DeepSeek V3.2 holds a shared-benchmark lead on LMArena Text Arena - Creative Writing, ahead by 167 points.
- DeepSeek V3.2 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- DeepSeek V3.2 has the lower cheapest tracked output price at $0.38/1M tokens.
- DeepSeek V3.2 uniquely exposes Code execution in local model data.
- Local decision data tags DeepSeek V3.2 for Coding, RAG, and Agents.
- Llama 3.1 70B Instruct has broader tracked provider coverage for fallback and route flexibility.
- Local decision data tags Llama 3.1 70B 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 3.1 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Estimated monthly gap: $124. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, NVIDIA NIM, and Microsoft Foundry; start route-level A/B tests there.
- Llama 3.1 70B Instruct is $0.02/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- 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 $0.02/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- DeepSeek V3.2 adds Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-01 | 2024-07-23 |
| Context window | 160K | 128K |
| Parameters | 671B | 70B |
| Architecture | Decoder Only | Decoder Only |
| License | MITOSI-approved | Llama 3 Community |
| Openness | Open source | Open weights |
| Weights | Unknown | Available |
| Code | Unknown | Unknown |
| Commercial use | Permitted | Restricted |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Pricing attribute | DeepSeek V3.2 | Llama 3.1 70B Instruct |
|---|---|---|
| Input price | $0.25/1M tokens | $0.40/1M tokens |
| Output price | $0.38/1M tokens | $0.40/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3.2 | Llama 3.1 70B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| JSON / 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 3.1 70B Instruct |
|---|---|---|
| LMArena Text Arena - Creative Writing | 1399.0 | 1232.0 |
| Chatbot Arena | 1425.0 | 1293.0 |
Deep dive
On shared benchmark coverage, LMArena Text Arena - Creative Writing has DeepSeek V3.2 at 1399 and Llama 3.1 70B Instruct at 1232, with DeepSeek V3.2 ahead by 167 points; Chatbot Arena has DeepSeek V3.2 at 1425 and Llama 3.1 70B Instruct at 1293, with DeepSeek V3.2 ahead by 132 points. The largest visible gap is 167 points on LMArena Text Arena - Creative Writing, 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 listed provider, while Llama 3.1 70B Instruct lists $0.40/1M input and $0.40/1M output tokens on the cheapest listed provider. A 70/30 input-output blend puts DeepSeek V3.2 lower by about $0.11 per million blended tokens. Availability is 8 providers versus 14, so concentration risk also matters.
Choose DeepSeek V3.2 when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose Llama 3.1 70B Instruct when provider fit 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 V3.2 or Llama 3.1 70B Instruct?
DeepSeek V3.2 supports 160K tokens, while Llama 3.1 70B 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 3.1 70B Instruct?
DeepSeek V3.2 is cheaper on listed token pricing. DeepSeek V3.2 costs $0.25/1M input and $0.38/1M output tokens. Llama 3.1 70B Instruct costs $0.40/1M input and $0.40/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.2 or Llama 3.1 70B Instruct open source?
DeepSeek V3.2 is listed under MIT. Llama 3.1 70B 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 3.1 70B Instruct?
Both DeepSeek V3.2 and Llama 3.1 70B 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 3.1 70B 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 3.1 70B Instruct?
DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, OpenRouter, and Microsoft Foundry. Llama 3.1 70B Instruct is available on Cloudflare Workers AI, OctoAI API (Deprecated), Together AI, Fireworks AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-10-20. Data sourced from public model cards and provider documentation.