Gemma 4 26B A4B vs Step 3.5 Flash
Head-to-head specifications
| Metric | Gemma 4 26B A4B | Step 3.5 Flash | Difference |
|---|---|---|---|
| Intelligence Index | 25.0 | 29.0 | -13.8% |
| Context window | 400K tokens | 262K tokens | — |
| Blended price ($/1M tokens) | $0.13 | $0.12 | +8.3% |
| Output speed (tokens/s) | 54 | 239 | -77.4% |
| Access | Open weights | Proprietary API | — |
- Step 3.5 Flash leads overall capability (Intelligence Index 29.0 vs 25.0).
- Step 3.5 Flash is the cheaper model to run at $0.12/1M blended tokens — about 1.1× cheaper.
- Gemma 4 26B A4B offers the larger context window (400K tokens), useful for long documents and codebases.
Verdict: Gemma 4 26B A4B or Step 3.5 Flash?
Gemma 4 26B A4B advantages
- Context window (+35%)
Step 3.5 Flash advantages
- General intelligence (+14%)
- Affordability (+8%)
- Output speed (+77%)
Which should you choose?
- Choose the Gemma 4 26B A4B if you work with long documents or large codebases.
- Choose the Step 3.5 Flash if you need the strongest overall reasoning and accuracy.
Value for money
Step 3.5 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.
Gemma 4 26B A4B vs Step 3.5 Flash: which should you choose?
Gemma 4 26B A4B — Google text model with an Intelligence Index of 25, a 400K-token context window and a blended price of $0.13/1M 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.
Gemma 4 26B A4B 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 25.0). Step 3.5 Flash is the cheaper model to run at $0.12/1M blended tokens — about 1.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Step 3.5 Flash scores 29.0 versus 25.0. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Gemma 4 26B A4B accepts up to 400K 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 54 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 $0.13 per 1M tokens). Gemma 4 26B A4B 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 Gemma 4 26B A4B better than the Step 3.5 Flash?
Step 3.5 Flash is the clearly stronger overall choice, winning most of the dimensions that matter. Step 3.5 Flash leads overall capability (Intelligence Index 29.0 vs 25.0).
What is the main difference between the Gemma 4 26B A4B and the Step 3.5 Flash?
Step 3.5 Flash leads overall capability (Intelligence Index 29.0 vs 25.0). Step 3.5 Flash is the cheaper model to run at $0.12/1M blended tokens — about 1.1× cheaper.
Which is better value?
Step 3.5 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 Gemma 4 26B A4B if you work with long documents or large codebases. Choose the Step 3.5 Flash if you need the strongest overall reasoning and accuracy.
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.