DeepSeek V3 vs Llama 4 Scout 17B-16E Instruct
DeepSeek V3 (2024) and Llama 4 Scout 17B-16E Instruct (2025) are compact production models from DeepSeek and AI at Meta. DeepSeek V3 ships a 64k-token context window, while Llama 4 Scout 17B-16E Instruct ships a 10m-token context window. On MMLU PRO, DeepSeek V3 leads by 1.6 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 fits 156x more tokens; pick it for long-context work and DeepSeek V3 for tighter calls.
Decision scorecard
Local evidence first| Signal | DeepSeek V3 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Best for | tool-calling agents and provider-routed production | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | Coding, Agents, and Classification | Coding, RAG, and Agents |
| Context window | 64k | 10m |
| Cheapest output | $0.30/1M tokens | $0.30/1M tokens |
| Provider routes | 13 tracked | 12 tracked |
| Shared benchmarks | MMLU PRO leader | 3 shared |
Decision tradeoffs
- DeepSeek V3 holds a shared-benchmark lead on MMLU PRO, ahead by 1.6 points.
- DeepSeek V3 has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek V3 uniquely exposes Function calling and Tool use in local model data.
- Local decision data tags DeepSeek V3 for Coding, Agents, and Classification.
- 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 uniquely exposes Vision and Multimodal in local model data.
- 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
$155
Cheapest tracked route/tier: Bitdeer AI
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $16.00. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, Together AI, and Fireworks AI; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
- Llama 4 Scout 17B-16E Instruct adds Vision and Multimodal in local capability data.
- Provider overlap exists on DeepInfra, Fireworks AI, and Microsoft Foundry; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- DeepSeek V3 adds Function calling and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-26 | 2025-04-05 |
| Context window | 64k | 10m |
| Parameters | 671B | 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 | 2024-04 | 2024-08 |
Pricing and availability
| Pricing attribute | DeepSeek V3 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | $0.10/1M tokens | $0.08/1M tokens |
| Output price | $0.30/1M tokens | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V3 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| MMLU PRO | 75.9 | 74.3 |
| LiveCodeBench | 49.6 | 32.8 |
| Chatbot Arena | 1302.0 | 1295.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V3 at 75.9 and Llama 4 Scout 17B-16E Instruct at 74.3, with DeepSeek V3 ahead by 1.6 points; LiveCodeBench has DeepSeek V3 at 49.6 and Llama 4 Scout 17B-16E Instruct at 32.8, with DeepSeek V3 ahead by 16.8 points; Chatbot Arena has DeepSeek V3 at 1302 and Llama 4 Scout 17B-16E Instruct at 1295, with DeepSeek V3 ahead by 7 points. The largest visible gap is 16.8 points on LiveCodeBench, 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: Llama 4 Scout 17B-16E Instruct, multimodal input: Llama 4 Scout 17B-16E Instruct, function calling: DeepSeek V3, and tool use: DeepSeek V3. 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 lists $0.10/1M input and $0.30/1M 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 $0.01 per million blended tokens. Availability is 13 providers versus 12, so concentration risk also matters.
Choose DeepSeek V3 when provider fit and broader provider choice 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 or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while DeepSeek V3 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 or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct is cheaper on tracked token pricing. DeepSeek V3 costs $0.10/1M input and $0.30/1M 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 or Llama 4 Scout 17B-16E Instruct open source?
DeepSeek V3 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 or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct 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 V3 or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct 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 and Llama 4 Scout 17B-16E Instruct?
DeepSeek V3 is available on DeepInfra, Fireworks AI, DeepSeek Platform, Microsoft Foundry, and OpenRouter. 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.
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Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.