Claude 4 Sonnet vs Step 3.5 Flash 2603
Head-to-head specifications
| Metric | Claude 4 Sonnet | Step 3.5 Flash 2603 | Difference |
|---|---|---|---|
| Intelligence Index | 29.0 | 29.0 | — |
| Context window | 1M tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $1.20 | $0.06 | +1,900.0% |
| Access | Proprietary API | Proprietary API | — |
- Claude 4 Sonnet leads overall capability (Intelligence Index 29.0 vs 29.0).
- Step 3.5 Flash 2603 is the cheaper model to run at $0.06/1M blended tokens — about 20.0× cheaper.
- Claude 4 Sonnet offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Claude 4 Sonnet or Step 3.5 Flash 2603?
Claude 4 Sonnet advantages
- Context window (+74%)
Step 3.5 Flash 2603 advantages
- Affordability (+95%)
Which should you choose?
- Choose the Claude 4 Sonnet if you work with long documents or large codebases.
- Choose the Step 3.5 Flash 2603 if you want the lowest cost per token at scale.
Value for money
Step 3.5 Flash 2603 offers more intelligence per dollar (20.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Claude 4 Sonnet vs Step 3.5 Flash 2603: which should you choose?
Claude 4 Sonnet — Anthropic multimodal model with an Intelligence Index of 29, a 1M-token context window and a blended price of $1.2/1M tokens.
Step 3.5 Flash 2603 — StepFun multimodal model with an Intelligence Index of 29, a 262K-token context window and a blended price of $0.06/1M tokens.
Claude 4 Sonnet vs Step 3.5 Flash 2603: Claude 4 Sonnet scores higher on the Intelligence Index. Claude 4 Sonnet leads overall capability (Intelligence Index 29.0 vs 29.0). Step 3.5 Flash 2603 is the cheaper model to run at $0.06/1M blended tokens — about 20.0× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Claude 4 Sonnet scores 29.0 versus 29.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Claude 4 Sonnet accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once.
Pricing and access
At blended per-token rates, Step 3.5 Flash 2603 is the cheaper model to run ($0.06 vs $1.20 per 1M tokens). Claude 4 Sonnet is proprietary api and Step 3.5 Flash 2603 is proprietary api. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Claude 4 Sonnet better than the Step 3.5 Flash 2603?
Step 3.5 Flash 2603 takes the overall edge, though Claude 4 Sonnet wins in specific areas worth weighing. Claude 4 Sonnet leads overall capability (Intelligence Index 29.0 vs 29.0).
What is the main difference between the Claude 4 Sonnet and the Step 3.5 Flash 2603?
Claude 4 Sonnet leads overall capability (Intelligence Index 29.0 vs 29.0). Step 3.5 Flash 2603 is the cheaper model to run at $0.06/1M blended tokens — about 20.0× cheaper.
Which is better value?
Step 3.5 Flash 2603 offers more intelligence per dollar (20.0× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Claude 4 Sonnet if you work with long documents or large codebases. Choose the Step 3.5 Flash 2603 if you want the lowest cost per token at scale.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.