DeepSeek V3.1 vs Llama 4 Scout 17B-16E Instruct
DeepSeek V3.1 (2025) and Llama 4 Scout 17B-16E Instruct (2025) are compact production models from DeepSeek and AI at Meta. DeepSeek V3.1 ships a 64k-token context window, while Llama 4 Scout 17B-16E Instruct ships a 10m-token context window. On MMLU PRO, DeepSeek V3.1 leads by 9 pts. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 4 Scout 17B-16E Instruct is ~238% cheaper at $0.08/1M; pay for DeepSeek V3.1 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.1 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Best for | multimodal apps and provider-routed production | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | Coding, Agents, and Vision | Coding, RAG, and Agents |
| Context window | 64k | 10m |
| Cheapest output | $1/1M tokens | $0.30/1M tokens |
| Provider routes | 8 tracked | 12 tracked |
| Shared benchmarks | MMLU PRO leader | 1 shared |
Decision tradeoffs
- DeepSeek V3.1 holds a shared-benchmark lead on MMLU PRO, ahead by 9 points.
- DeepSeek V3.1 uniquely exposes Code execution in local model data.
- Local decision data tags DeepSeek V3.1 for Coding, Agents, and Vision.
- Llama 4 Scout 17B-16E Instruct has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Llama 4 Scout 17B-16E Instruct has the lower cheapest tracked output price at $0.30/1M tokens.
- Llama 4 Scout 17B-16E Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 4 Scout 17B-16E Instruct for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek V3.1
$466
Cheapest tracked route/tier: Novita AI
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $327. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Together AI, Fireworks AI, and NVIDIA NIM; start route-level A/B tests there.
- Llama 4 Scout 17B-16E Instruct is $0.70/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Code execution before moving production traffic.
- Provider overlap exists on Microsoft Foundry, Fireworks AI, and NVIDIA NIM; start route-level A/B tests there.
- DeepSeek V3.1 is $0.70/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- DeepSeek V3.1 adds Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-08-21 | 2025-04-05 |
| Context window | 64k | 10m |
| Parameters | 671B total, 37B active (MoE) | 109B (17B active) |
| Architecture | Mixture of Experts | Mixture of Experts |
| License | MITOSI-approved | Llama 4 Community |
| Openness | Open source | Open weights |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| Knowledge cutoff | - | 2024-08 |
Pricing and availability
| Pricing attribute | DeepSeek V3.1 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | $0.27/1M tokens | $0.08/1M tokens |
| Output price | $1/1M tokens | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3.1 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | 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 V3.1 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| MMLU PRO | 83.3 | 74.3 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V3.1 at 83.3 and Llama 4 Scout 17B-16E Instruct at 74.3, with DeepSeek V3.1 ahead by 9 points. The largest visible gap is 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 code execution: DeepSeek V3.1. Both models share vision, multimodal input, and 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 $0.27/1M input and $1/1M output tokens on the cheapest tracked provider, while Llama 4 Scout 17B-16E Instruct lists $0.08/1M input and $0.30/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 4 Scout 17B-16E Instruct lower by about $0.34 per million blended tokens. Availability is 8 providers versus 12, so concentration risk also matters.
Choose DeepSeek V3.1 when coding workflow support are central to the workload. Choose Llama 4 Scout 17B-16E Instruct when long-context analysis, larger context windows, and lower input-token cost 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 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while DeepSeek V3.1 supports 64k 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 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct is cheaper on tracked token pricing. DeepSeek V3.1 costs $0.27/1M input and $1/1M output tokens. Llama 4 Scout 17B-16E Instruct costs $0.08/1M input and $0.30/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.1 or Llama 4 Scout 17B-16E Instruct open source?
DeepSeek V3.1 is listed under MIT. Llama 4 Scout 17B-16E Instruct is listed under Llama 4 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 V3.1 or Llama 4 Scout 17B-16E Instruct?
Both DeepSeek V3.1 and Llama 4 Scout 17B-16E Instruct expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for multimodal input, DeepSeek V3.1 or Llama 4 Scout 17B-16E Instruct?
Both DeepSeek V3.1 and Llama 4 Scout 17B-16E Instruct expose multimodal input. 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 V3.1 and Llama 4 Scout 17B-16E Instruct?
DeepSeek V3.1 is available on Microsoft Foundry, Fireworks AI, NVIDIA NIM, Together AI, and AWS Bedrock. Llama 4 Scout 17B-16E Instruct is available on Cloudflare Workers AI, OpenRouter, Together AI, Fireworks AI, and DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.