Claude 3.5 Haiku vs Llama 4 Scout 17B-16E Instruct
Claude 3.5 Haiku (2024) and Llama 4 Scout 17B-16E Instruct (2025) are frontier reasoning models from Anthropic and AI at Meta. Claude 3.5 Haiku ships a 200k-token context window, while Llama 4 Scout 17B-16E Instruct ships a 10m-token context window. On pricing, Llama 4 Scout 17B-16E Instruct costs $0.08/1M input tokens versus $0.80/1M for the alternative. 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 ~900% cheaper at $0.08/1M; pay for Claude 3.5 Haiku only for coding workflow support.
Decision scorecard
Local evidence first| Signal | Claude 3.5 Haiku | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and provider-routed production | 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 | $4/1M tokens | $0.30/1M tokens |
| Provider routes | 6 tracked | 12 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Claude 3.5 Haiku uniquely exposes Reasoning and Code execution in local model data.
- Local decision data tags Claude 3.5 Haiku 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 route flexibility.
- Llama 4 Scout 17B-16E Instruct uniquely exposes 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.
Claude 3.5 Haiku
$1,640
Cheapest tracked route/tier: Anthropic
Llama 4 Scout 17B-16E Instruct
$139
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $1,501. 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 $3.70/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning and Code execution before moving production traffic.
- Llama 4 Scout 17B-16E Instruct adds Multimodal in local capability data.
- Provider overlap exists on OpenRouter, AWS Bedrock, and GCP Vertex AI; start route-level A/B tests there.
- Claude 3.5 Haiku is $3.70/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Multimodal before moving production traffic.
- Claude 3.5 Haiku adds Reasoning and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-10-22 | 2025-04-05 |
| Context window | 200k | 10m |
| Parameters | — | 109B (17B active) |
| Architecture | Decoder Only | Mixture of Experts |
| License | Proprietary | Llama 4 Community |
| Openness | Proprietary | Open weights |
| Weights | Not released | Unknown |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: conditional | Commercial use: conditional |
| Knowledge cutoff | - | 2024-08 |
Pricing and availability
| Pricing attribute | Claude 3.5 Haiku | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | $0.80/1M tokens | $0.08/1M tokens |
| Output price | $4/1M tokens | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | Claude 3.5 Haiku | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | 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
No shared benchmark scores are currently available for this pair.
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Last reviewed: 2026-07-26. Data sourced from public model cards and provider documentation.