Claude 3.5 Sonnet vs Claude Sonnet 4.6
Claude 3.5 Sonnet (2024) and Claude Sonnet 4.6 (2026) are frontier-tier reasoning models from Anthropic. Claude 3.5 Sonnet ships a 200k-token context window, while Claude Sonnet 4.6 ships a 1m-token context window. On MMLU PRO, Claude Sonnet 4.6 leads by 10.1 pts. On pricing, both list $3/1M input and $15/1M output tokens on the cheapest tracked route. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Pick Claude Sonnet 4.6 for general evaluation; token pricing is tied, so keep Claude 3.5 Sonnet only for already-validated prompts or route constraints.
Decision scorecard
Local evidence first| Signal | Claude 3.5 Sonnet | Claude Sonnet 4.6 |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 200k | 1m |
| Cheapest output | $15/1M tokens | $15/1M tokens |
| Provider routes | 6 tracked | 6 tracked |
| Shared benchmarks | 8 shared | MMLU PRO leader |
Decision tradeoffs
- Local decision data tags Claude 3.5 Sonnet for Coding, RAG, and Agents.
- Claude Sonnet 4.6 holds a shared-benchmark lead on MMLU PRO, ahead by 10.1 points.
- Claude Sonnet 4.6 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Claude Sonnet 4.6 uniquely exposes Computer use and Parallel agents in local model data.
- Local decision data tags Claude Sonnet 4.6 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
Claude Sonnet 4.6
$6,150
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $0.00. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, Anthropic, and AWS Bedrock; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Claude Sonnet 4.6 adds Computer use and Parallel agents in local capability data.
- Use this scaffold for teams deciding whether Sonnet 4.6 should replace an established Claude 3.5 Sonnet integration.
- Focus on coding-agent reliability, long-context behavior, and output-token price deltas.
- Mention that migration should be tested with existing system prompts because Claude family upgrades can change refusal and formatting behavior.
- Provider overlap exists on GCP Vertex AI, AWS Bedrock, and Anthropic; 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 Computer use and Parallel agents before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-06-20 | 2026-02-17 |
| Context window | 200k | 1m |
| Parameters | 70B | — |
| Architecture | Decoder Only | Decoder Only |
| License | Proprietary | Proprietary |
| Openness | Proprietary | Proprietary |
| Weights | Not released | Not released |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: conditional | Commercial use: conditional |
| Knowledge cutoff | 2024-04 | 2025-08 |
Pricing and availability
| Pricing attribute | Claude 3.5 Sonnet | Claude Sonnet 4.6 |
|---|---|---|
| Input price | $3/1M tokens | $3/1M tokens |
| Output price | $15/1M tokens | $15/1M tokens |
| Providers |
Capabilities
| Capability | Claude 3.5 Sonnet | Claude Sonnet 4.6 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | Yes |
| JSON / Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | Yes |
| IDE integration | No | No |
| Computer use | No | Yes |
| Parallel agents | No | Yes |
Benchmarks
| Benchmark | Claude 3.5 Sonnet | Claude Sonnet 4.6 |
|---|---|---|
| MMLU PRO | 77.2 | 87.3 |
| SWE-bench Verified | 49.0 | 79.6 |
| LiveCodeBench | 48.7 | 80.0 |
| Massive Multi-discipline Multimodal Understanding | 68.3 | 83.6 |
| HumanEval | 92.0 | 98.0 |
| Chatbot Arena | 1340.0 | 1459.0 |
| MMMU Pro | 63.7 | 75.6 |
| Massive Multitask Language Understanding | 88.7 | 89.3 |
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Last reviewed: 2026-07-26. Data sourced from public model cards and provider documentation.