Gemma 4 26B A4B vs Qwen3.6 Max Preview
Head-to-head specifications
| Metric | Gemma 4 26B A4B | Qwen3.6 Max Preview | Difference |
|---|---|---|---|
| Intelligence Index | 25.0 | 40.0 | -37.5% |
| Context window | 400K tokens | 400K tokens | — |
| Blended price ($/1M tokens) | $0.13 | $0.70 | -81.4% |
| Output speed (tokens/s) | 54 | 42 | +28.6% |
| Access | Open weights | Open weights | — |
- Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 25.0).
- Gemma 4 26B A4B is the cheaper model to run at $0.13/1M blended tokens — about 5.4× cheaper.
Verdict: Gemma 4 26B A4B or Qwen3.6 Max Preview?
Gemma 4 26B A4B advantages
- Affordability (+81%)
- Output speed (+22%)
Qwen3.6 Max Preview advantages
- General intelligence (+38%)
Which should you choose?
- Choose the Gemma 4 26B A4B if you want the lowest cost per token at scale.
- Choose the Qwen3.6 Max Preview if you need the strongest overall reasoning and accuracy.
- Choose the Gemma 4 26B A4B if low latency and fast generation matter for your application.
Value for money
Gemma 4 26B A4B offers more intelligence per dollar (3.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Gemma 4 26B A4B vs Qwen3.6 Max Preview: 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).
Qwen3.6 Max Preview — Alibaba multimodal model with an Intelligence Index of 40, a 400K-token context window and a blended price of $0.7/1M tokens (open weights).
Gemma 4 26B A4B vs Qwen3.6 Max Preview: Qwen3.6 Max Preview scores higher on the Intelligence Index. Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 25.0). Gemma 4 26B A4B is the cheaper model to run at $0.13/1M blended tokens — about 5.4× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the Qwen3.6 Max Preview scores 40.0 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, Gemma 4 26B A4B generates faster (54 vs 42 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, Gemma 4 26B A4B is the cheaper model to run ($0.13 vs $0.70 per 1M tokens). Gemma 4 26B A4B is open weights and Qwen3.6 Max Preview is open weights. 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 Qwen3.6 Max Preview?
These two are closely matched — the right pick comes down to which specific strengths you value and the price you actually pay. Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 25.0).
What is the main difference between the Gemma 4 26B A4B and the Qwen3.6 Max Preview?
Qwen3.6 Max Preview leads overall capability (Intelligence Index 40.0 vs 25.0). Gemma 4 26B A4B is the cheaper model to run at $0.13/1M blended tokens — about 5.4× cheaper.
Which is better value?
Gemma 4 26B A4B offers more intelligence per dollar (3.4× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the Gemma 4 26B A4B if you want the lowest cost per token at scale. Choose the Qwen3.6 Max Preview 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.