Kimi K2 0905 vs Step 3.5 Flash
Head-to-head specifications
| Metric | Kimi K2 0905 | Step 3.5 Flash | Difference |
|---|---|---|---|
| Intelligence Index | 28.0 | 29.0 | -3.4% |
| Context window | 300K tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.62 | $0.12 | +416.7% |
| Output speed (tokens/s) | 36 | 239 | -84.9% |
| Access | Open weights | Proprietary API | — |
- Step 3.5 Flash leads overall capability (Intelligence Index 29.0 vs 28.0).
- Step 3.5 Flash is the cheaper model to run at $0.12/1M blended tokens — about 5.2× cheaper.
- Kimi K2 0905 offers the larger context window (300K tokens), useful for long documents and codebases.
Verdict: Kimi K2 0905 or Step 3.5 Flash?
Kimi K2 0905 advantages
- Context window (+13%)
Step 3.5 Flash advantages
- Affordability (+81%)
- Output speed (+85%)
Which should you choose?
- Choose the Kimi K2 0905 if you work with long documents or large codebases.
- Choose the Step 3.5 Flash if you want the lowest cost per token at scale.
Value for money
Step 3.5 Flash offers more intelligence per dollar (5.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Kimi K2 0905 vs Step 3.5 Flash: which should you choose?
Kimi K2 0905 — Moonshot AI text model with an Intelligence Index of 28, a 300K-token context window and a blended price of $0.62/1M tokens (open weights).
Step 3.5 Flash — StepFun multimodal model with an Intelligence Index of 29, a 262K-token context window and a blended price of $0.12/1M tokens.
Kimi K2 0905 vs Step 3.5 Flash: Step 3.5 Flash scores higher on the Intelligence Index. Step 3.5 Flash leads overall capability (Intelligence Index 29.0 vs 28.0). Step 3.5 Flash is the cheaper model to run at $0.12/1M blended tokens — about 5.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Step 3.5 Flash scores 29.0 versus 28.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Kimi K2 0905 accepts up to 300K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, Step 3.5 Flash generates faster (239 vs 36 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Step 3.5 Flash is the cheaper model to run ($0.12 vs $0.62 per 1M tokens). Kimi K2 0905 is open weights and Step 3.5 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 Kimi K2 0905 better than the Step 3.5 Flash?
Step 3.5 Flash takes the overall edge, though Kimi K2 0905 wins in specific areas worth weighing. Step 3.5 Flash leads overall capability (Intelligence Index 29.0 vs 28.0).
What is the main difference between the Kimi K2 0905 and the Step 3.5 Flash?
Step 3.5 Flash leads overall capability (Intelligence Index 29.0 vs 28.0). Step 3.5 Flash is the cheaper model to run at $0.12/1M blended tokens — about 5.2× cheaper.
Which is better value?
Step 3.5 Flash offers more intelligence per dollar (5.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Which should I choose?
Choose the Kimi K2 0905 if you work with long documents or large codebases. Choose the Step 3.5 Flash 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.