Gemma 4 26B A4B vs o3-pro
Head-to-head specifications
| Metric | Gemma 4 26B A4B | o3-pro | Difference |
|---|---|---|---|
| Intelligence Index | 25.0 | 33.0 | -24.2% |
| Context window | 400K tokens | 258K tokens | — |
| Blended price ($/1M tokens) | $0.13 | $1.87 | -93.0% |
| Output speed (tokens/s) | 54 | 42 | +28.6% |
| Access | Open weights | Proprietary API | — |
- o3-pro leads overall capability (Intelligence Index 33.0 vs 25.0).
- Gemma 4 26B A4B is the cheaper model to run at $0.13/1M blended tokens — about 14.4× cheaper.
- Gemma 4 26B A4B offers the larger context window (400K tokens), useful for long documents and codebases.
Verdict: Gemma 4 26B A4B or o3-pro?
Gemma 4 26B A4B advantages
- Context window (+36%)
- Affordability (+93%)
- Output speed (+22%)
o3-pro advantages
- General intelligence (+24%)
Which should you choose?
- Choose the Gemma 4 26B A4B if you work with long documents or large codebases.
- Choose the o3-pro if you need the strongest overall reasoning and accuracy.
- Choose the Gemma 4 26B A4B if you want the lowest cost per token at scale.
Value for money
Gemma 4 26B A4B offers more intelligence per dollar (10.9× 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 o3-pro: 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).
o3-pro — OpenAI multimodal model with an Intelligence Index of 33, a 258K-token context window and a blended price of $1.87/1M tokens.
Gemma 4 26B A4B vs o3-pro: o3-pro scores higher on the Intelligence Index. o3-pro leads overall capability (Intelligence Index 33.0 vs 25.0). Gemma 4 26B A4B is the cheaper model to run at $0.13/1M blended tokens — about 14.4× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the o3-pro scores 33.0 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 $1.87 per 1M tokens). Gemma 4 26B A4B is open weights and o3-pro 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 o3-pro?
Gemma 4 26B A4B takes the overall edge, though o3-pro wins in specific areas worth weighing. o3-pro leads overall capability (Intelligence Index 33.0 vs 25.0).
What is the main difference between the Gemma 4 26B A4B and the o3-pro?
o3-pro leads overall capability (Intelligence Index 33.0 vs 25.0). Gemma 4 26B A4B is the cheaper model to run at $0.13/1M blended tokens — about 14.4× cheaper.
Which is better value?
Gemma 4 26B A4B offers more intelligence per dollar (10.9× 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 work with long documents or large codebases. Choose the o3-pro 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.