DeepSeek V4 Pro vs Llama 4 Scout 17B-16E Instruct
DeepSeek V4 Pro (2026) and Llama 4 Scout 17B-16E Instruct (2025) are frontier reasoning models from DeepSeek and AI at Meta. DeepSeek V4 Pro ships a 1m-token context window, while Llama 4 Scout 17B-16E Instruct ships a 10m-token context window. On MMLU PRO, DeepSeek V4 Pro leads by 13.2 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 ~444% cheaper at $0.08/1M; pay for DeepSeek V4 Pro only for reasoning depth.
Decision scorecard
Local evidence first| Signal | DeepSeek V4 Pro | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and long-context analysis | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1m | 10m |
| Cheapest output | $0.87/1M tokens | $0.30/1M tokens |
| Provider routes | 5 tracked | 12 tracked |
| Shared benchmarks | MMLU PRO leader | 3 rows |
Decision tradeoffs
- DeepSeek V4 Pro holds a shared-benchmark lead on MMLU PRO, ahead by 13.2 points.
- DeepSeek V4 Pro uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags DeepSeek V4 Pro for Coding, RAG, and Agents.
- 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.
- 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 V4 Pro
$566
Cheapest tracked route/tier: DeepSeek Platform
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $427. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, Fireworks AI, and Vercel AI Gateway; start route-level A/B tests there.
- Llama 4 Scout 17B-16E Instruct is $0.57/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, 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 Fireworks AI, OpenRouter, and Vercel AI Gateway; start route-level A/B tests there.
- DeepSeek V4 Pro is $0.57/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- DeepSeek V4 Pro adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-24 | 2025-04-05 |
| Context window | 1m | 10m |
| Parameters | 1.6T | 109B (17B active) |
| Architecture | Mixture of Experts (MoE) with CSA+HCA hybrid attention | mixture of experts |
| License | MIT(OSI) | Llama 4 Community |
| Openness | Open source | Open weights |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | - | 2024-08 |
Pricing and availability
| Pricing attribute | DeepSeek V4 Pro | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | $0.43/1M tokens | $0.08/1M tokens |
| Output price | $0.87/1M tokens | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V4 Pro | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | 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 V4 Pro | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| MMLU PRO | 87.5 | 74.3 |
| LiveCodeBench | 93.5 | 32.8 |
| Chatbot Arena | 1460.0 | 1295.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V4 Pro at 87.5 and Llama 4 Scout 17B-16E Instruct at 74.3, with DeepSeek V4 Pro ahead by 13.2 points; LiveCodeBench has DeepSeek V4 Pro at 93.5 and Llama 4 Scout 17B-16E Instruct at 32.8, with DeepSeek V4 Pro ahead by 60.7 points; Chatbot Arena has DeepSeek V4 Pro at 1460 and Llama 4 Scout 17B-16E Instruct at 1295, with DeepSeek V4 Pro ahead by 165 points. The largest visible gap is 165 points on Chatbot Arena, 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, reasoning mode: DeepSeek V4 Pro, function calling: DeepSeek V4 Pro, and tool use: DeepSeek V4 Pro. 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 V4 Pro lists $0.43/1M input and $0.87/1M 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 $0.42 per million blended tokens. Availability is 5 providers versus 12, so concentration risk also matters.
Choose DeepSeek V4 Pro when reasoning depth 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 V4 Pro or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while DeepSeek V4 Pro supports 1m 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 V4 Pro or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct is cheaper on tracked token pricing. DeepSeek V4 Pro costs $0.43/1M input and $0.87/1M 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 V4 Pro or Llama 4 Scout 17B-16E Instruct open source?
DeepSeek V4 Pro 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 V4 Pro 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 V4 Pro 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 V4 Pro and Llama 4 Scout 17B-16E Instruct?
DeepSeek V4 Pro is available on DeepSeek Platform, Fireworks AI, OpenRouter, Vercel AI Gateway, and Novita AI. 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: 2026-06-07. Data sourced from public model cards and provider documentation.