Comparison · Updated Aug 24, 2026
Claude 3.5 Sonnet vs Claude 3 Opus
Claude 3.5 Sonnet (June 2024) and Claude 3 Opus (March 2024) are Anthropic LLMs with 200k‑token context, vision, and coding; Sonnet emphasizes agentic tool use, Opus emphasizes reasoning.
Model A
Claude 3.5 Sonnet
Mid-tier 3.5 model that outperformed Claude 3 Opus on many tasks.
Jun 20, 2024
Model B
Claude 3 Opus
Top-tier model in the Claude 3 lineup with strong reasoning.
Mar 4, 2024
Differences
| Attribute | Claude 3.5 Sonnet | Claude 3 Opus |
|---|---|---|
| Developer | Anthropic | Anthropic |
| Released | 2024-06-20 | 2024-03-04 |
| Open weights | false | false |
| Context window | 200000 tokens | 200000 tokens |
| Modality | Multimodal (text & vision) | Multimodal (text & vision) |
| Capabilities | coding, agentic tool use, vision | reasoning, vision, coding |
| Best for | coding workflows, agentic tool use, general‑purpose tasks | reasoning‑heavy tasks, complex problem solving |
Verdict
Readers prioritizing strong reasoning and complex problem‑solving may prefer Claude 3 Opus, while those focused on coding workflows, agentic tool integration, or general‑purpose performance may find Claude 3.5 Sonnet more suitable.
Analysis
Overview Claude 3.5 Sonnet and Claude 3 Opus are both large language models developed by Anthropic. They share several core characteristics: neither model has open weights, each supports a context window of 200,000 tokens, and both are multimodal, accepting text and image inputs. The models were released within a few months of each other, with Opus arriving in March 2024 and Sonnet following in June 2024. Anthropic positions Opus as the top‑tier model in the Claude 3 lineup, highlighting its strong reasoning abilities, while describing Sonnet as a mid‑tier model that, according to the company’s own statement, outperformed Opus on many tasks.
Where they differ The most notable differences lie in their release dates and emphasized capabilities. Opus was released earlier, on 2024-03-04, and its documented capabilities center on reasoning, vision, and coding. Sonnet, released on 2024-06-20, is noted for coding, agentic tool use, and vision. Both models support vision, but Opus places explicit emphasis on reasoning, whereas Sonnet highlights agentic tool use—a feature that enables the model to interact with external tools and APIs in an autonomous fashion. The description provided for each model also reflects a tier distinction: Opus is labeled as the top‑tier model, while Sonnet is characterized as mid‑tier, with the claim that Sonnet outperforms Opus on many tasks despite its tier placement.
Which to choose Choosing between the two depends on the primary use case. If the workload requires deep logical reasoning, complex inference, or tasks where reasoning ability is a bottleneck, Claude 3 Opus may be the more appropriate option due to its explicit focus on reasoning strengths. Conversely, if the workflow benefits from tight integration with external tools, agentic behavior, or general coding assistance, Claude 3.5 Sonnet’s emphasis on agentic tool use and coding could provide a better fit. Both models offer the same extensive context window and multimodal input handling, so the decision hinges on the specific capability set that aligns with the user’s objectives.
Frequently asked
- Which model has a larger context window?
- Both Claude 3.5 Sonnet and Claude 3 Opus support a context window of 200,000 tokens, so there is no difference in this regard.
- When were the two models released?
- Claude 3 Opus was released earlier, on March 4, 2024, while Claude 3.5 Sonnet launched later, on June 20, 2024.
- Are the weights for Claude 3.5 Sonnet or Claude 3 Opus publicly available?
- Neither model has open weights; both are proprietary models distributed by Anthropic under standard usage terms.
- What are the main capability differences between the two models?
- Claude 3 Opus is described as having strong reasoning abilities, whereas Claude 3.5 Sonnet is highlighted for coding, agentic tool use, and vision capabilities.
- Do the models support vision input?
- Both models accept text and image inputs, making them multimodal. This allows them to process visual data alongside textual prompts for tasks such as image captioning or visual question answering.