Claude 3.5 Sonnet vs Llama 4 Scout 17B-16E Instruct
Claude 3.5 Sonnet (2024) and Llama 4 Scout 17B-16E Instruct (2025) are frontier reasoning models from Anthropic and AI at Meta. Claude 3.5 Sonnet ships a 200k-token context window, while Llama 4 Scout 17B-16E Instruct ships a 10m-token context window. On MMLU PRO, Claude 3.5 Sonnet leads by 2.9 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 ~3650% cheaper at $0.08/1M; pay for Claude 3.5 Sonnet only for coding workflow support.
Decision scorecard
Local evidence first| Signal | Claude 3.5 Sonnet | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | multimodal apps, long-context analysis, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 200k | 10m |
| Cheapest output | $15/1M tokens | $0.30/1M tokens |
| Provider routes | 6 tracked | 12 tracked |
| Shared benchmarks | MMLU PRO leader | 4 shared |
Decision tradeoffs
- Claude 3.5 Sonnet holds a shared-benchmark lead on MMLU PRO, ahead by 2.9 points.
- Claude 3.5 Sonnet uniquely exposes Reasoning, Function calling, and Code execution in local model data.
- Local decision data tags Claude 3.5 Sonnet for Coding, RAG, and Agents.
- Llama 4 Scout 17B-16E Instruct holds a shared-benchmark lead on Massive Multi-discipline Multimodal Understanding, ahead by 1.1 points.
- 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.
Claude 3.5 Sonnet
$6,150
Cheapest tracked route/tier: GCP Vertex AI
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $6,011. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, GCP Vertex AI, and AWS Bedrock; start route-level A/B tests there.
- Llama 4 Scout 17B-16E Instruct is $14.70/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Code execution before moving production traffic.
- Provider overlap exists on GCP Vertex AI, AWS Bedrock, and OpenRouter; start route-level A/B tests there.
- Claude 3.5 Sonnet is $14.70/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Claude 3.5 Sonnet adds Reasoning, Function calling, and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-06-20 | 2025-04-05 |
| Context window | 200k | 10m |
| Parameters | 70B | 109B (17B active) |
| Architecture | Decoder Only | Mixture of Experts |
| License | Proprietary | Llama 4 Community |
| Openness | Proprietary | Open weights |
| Commercial use | Commercial use: conditional | Commercial use: conditional |
| Knowledge cutoff | 2024-04 | 2024-08 |
Pricing and availability
| Pricing attribute | Claude 3.5 Sonnet | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | $3/1M tokens | $0.08/1M tokens |
| Output price | $15/1M tokens | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | Claude 3.5 Sonnet | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | 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 | Claude 3.5 Sonnet | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| MMLU PRO | 77.2 | 74.3 |
| LiveCodeBench | 48.7 | 32.8 |
| Massive Multi-discipline Multimodal Understanding | 68.3 | 69.4 |
| Chatbot Arena | 1340.0 | 1295.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has Claude 3.5 Sonnet at 77.2 and Llama 4 Scout 17B-16E Instruct at 74.3, with Claude 3.5 Sonnet ahead by 2.9 points; LiveCodeBench has Claude 3.5 Sonnet at 48.7 and Llama 4 Scout 17B-16E Instruct at 32.8, with Claude 3.5 Sonnet ahead by 15.9 points; Massive Multi-discipline Multimodal Understanding has Claude 3.5 Sonnet at 68.3 and Llama 4 Scout 17B-16E Instruct at 69.4, with Llama 4 Scout 17B-16E Instruct ahead by 1.1 points. The largest visible gap is 15.9 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 reasoning mode: Claude 3.5 Sonnet, function calling: Claude 3.5 Sonnet, and code execution: Claude 3.5 Sonnet. 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, Claude 3.5 Sonnet lists $3/1M input and $15/1M 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 $6.45 per million blended tokens. Availability is 6 providers versus 12, so concentration risk also matters.
Choose Claude 3.5 Sonnet 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, Claude 3.5 Sonnet or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct supports 10m tokens, while Claude 3.5 Sonnet supports 200k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Claude 3.5 Sonnet or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct is cheaper on tracked token pricing. Claude 3.5 Sonnet costs $3/1M input and $15/1M 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 Claude 3.5 Sonnet or Llama 4 Scout 17B-16E Instruct open source?
Claude 3.5 Sonnet is listed under Proprietary. 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, Claude 3.5 Sonnet or Llama 4 Scout 17B-16E Instruct?
Both Claude 3.5 Sonnet 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, Claude 3.5 Sonnet or Llama 4 Scout 17B-16E Instruct?
Both Claude 3.5 Sonnet 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 Claude 3.5 Sonnet and Llama 4 Scout 17B-16E Instruct?
Claude 3.5 Sonnet is available on GCP Vertex AI, AWS Bedrock, Anthropic, OpenRouter, and Microsoft Foundry. 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.