GPT-5 mini vs Step 3.7 Flash
Head-to-head specifications
| Metric | GPT-5 mini | Step 3.7 Flash | Difference |
|---|---|---|---|
| Intelligence Index | 32.0 | 31.0 | +3.2% |
| Coding Index | 15.6 | 39.6 | -60.6% |
| Agentic Index | 19.4 | 21.5 | — |
| Context window | 922K tokens | 400K tokens | — |
| Blended price ($/1M tokens) | $0.26 | $0.20 | +30.0% |
| Output speed (tokens/s) | 93 | 383 | -75.7% |
| Access | Proprietary API | Proprietary API | — |
- GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 31.0).
- Step 3.7 Flash is the cheaper model to run at $0.20/1M blended tokens — about 1.3× cheaper.
- GPT-5 mini offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: GPT-5 mini or Step 3.7 Flash?
GPT-5 mini advantages
- Context window (+57%)
Step 3.7 Flash advantages
- Coding ability (+61%)
- Agentic task performance (+10%)
- Affordability (+23%)
- Output speed (+76%)
Which should you choose?
- Choose the GPT-5 mini if you work with long documents or large codebases.
- Choose the Step 3.7 Flash if coding and software development are your main workload.
Value for money
Step 3.7 Flash offers more intelligence per dollar (1.3× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
GPT-5 mini vs Step 3.7 Flash: which should you choose?
GPT-5 mini — OpenAI multimodal model with an Intelligence Index of 32, a 922K-token context window and a blended price of $0.26/1M tokens.
Step 3.7 Flash — StepFun multimodal model with an Intelligence Index of 31, a 400K-token context window and a blended price of $0.2/1M tokens.
GPT-5 mini vs Step 3.7 Flash: GPT-5 mini scores higher on the Intelligence Index. GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 31.0). Step 3.7 Flash is the cheaper model to run at $0.20/1M blended tokens — about 1.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5 mini scores 32.0 versus 31.0. For software development, the Coding Index puts Step 3.7 Flash ahead (39.6 vs 15.6). On agentic, multi-step tool-use tasks, Step 3.7 Flash measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5 mini accepts up to 922K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Step 3.7 Flash generates faster (383 vs 93 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Step 3.7 Flash is the cheaper model to run ($0.20 vs $0.26 per 1M tokens). GPT-5 mini is proprietary api and Step 3.7 Flash 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 GPT-5 mini better than the Step 3.7 Flash?
Step 3.7 Flash is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 31.0).
What is the main difference between the GPT-5 mini and the Step 3.7 Flash?
GPT-5 mini leads overall capability (Intelligence Index 32.0 vs 31.0). Step 3.7 Flash is the cheaper model to run at $0.20/1M blended tokens — about 1.3× cheaper.
Which is better value?
Step 3.7 Flash offers more intelligence per dollar (1.3× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the GPT-5 mini if you work with long documents or large codebases. Choose the Step 3.7 Flash if coding and software development are your main workload.
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.