Gemma 4 26B A4B vs GPT-5.4 nano
Head-to-head specifications
| Metric | Gemma 4 26B A4B | GPT-5.4 nano | Difference |
|---|---|---|---|
| Intelligence Index | 25.0 | 38.0 | -34.2% |
| Coding Index | 39.3 | 56.1 | -29.9% |
| Agentic Index | 11.0 | 27.5 | — |
| Context window | 400K tokens | 922K tokens | — |
| Blended price ($/1M tokens) | $0.13 | $0.18 | -27.8% |
| Output speed (tokens/s) | 54 | 157 | -65.6% |
| Access | Open weights | Proprietary API | — |
- GPT-5.4 nano leads overall capability (Intelligence Index 38.0 vs 25.0).
- Gemma 4 26B A4B is the cheaper model to run at $0.13/1M blended tokens — about 1.4× cheaper.
- GPT-5.4 nano offers the larger context window (922K tokens), useful for long documents and codebases.
Verdict: Gemma 4 26B A4B or GPT-5.4 nano?
Gemma 4 26B A4B advantages
- Affordability (+28%)
GPT-5.4 nano advantages
- General intelligence (+34%)
- Coding ability (+30%)
- Agentic task performance (+60%)
- Context window (+57%)
- Output speed (+66%)
Which should you choose?
- Choose the Gemma 4 26B A4B if you want the lowest cost per token at scale.
- Choose the GPT-5.4 nano if you need the strongest overall reasoning and accuracy.
Value for money
GPT-5.4 nano offers more intelligence per dollar (1.1× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use.
Gemma 4 26B A4B vs GPT-5.4 nano: 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).
GPT-5.4 nano — OpenAI multimodal model with an Intelligence Index of 38, a 922K-token context window and a blended price of $0.18/1M tokens.
Gemma 4 26B A4B vs GPT-5.4 nano: GPT-5.4 nano scores higher on the Intelligence Index. GPT-5.4 nano leads overall capability (Intelligence Index 38.0 vs 25.0). Gemma 4 26B A4B is the cheaper model to run at $0.13/1M blended tokens — about 1.4× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.4 nano scores 38.0 versus 25.0. For software development, the Coding Index puts GPT-5.4 nano ahead (56.1 vs 39.3). On agentic, multi-step tool-use tasks, GPT-5.4 nano measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.4 nano accepts up to 922K tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GPT-5.4 nano generates faster (157 vs 54 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 $0.18 per 1M tokens). Gemma 4 26B A4B is open weights and GPT-5.4 nano 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 GPT-5.4 nano?
GPT-5.4 nano is the clearly stronger overall choice, winning most of the dimensions that matter. GPT-5.4 nano leads overall capability (Intelligence Index 38.0 vs 25.0).
What is the main difference between the Gemma 4 26B A4B and the GPT-5.4 nano?
GPT-5.4 nano leads overall capability (Intelligence Index 38.0 vs 25.0). Gemma 4 26B A4B is the cheaper model to run at $0.13/1M blended tokens — about 1.4× cheaper.
Which is better value?
GPT-5.4 nano offers more intelligence per dollar (1.1× 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 want the lowest cost per token at scale. Choose the GPT-5.4 nano 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.