Gemma 4 26B A4B vs GLM-5
Head-to-head specifications
| Metric | Gemma 4 26B A4B | GLM-5 | Difference |
|---|---|---|---|
| Intelligence Index | 25.0 | 33.0 | -24.2% |
| Context window | 400K tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $0.13 | $0.52 | -75.0% |
| Output speed (tokens/s) | 54 | 46 | +17.4% |
| Access | Open weights | Open weights | — |
- GLM-5 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 4.0× cheaper.
- Gemma 4 26B A4B offers the larger context window (400K tokens), useful for long documents and codebases.
Verdict: Gemma 4 26B A4B or GLM-5?
Gemma 4 26B A4B advantages
- Context window (+36%)
- Affordability (+75%)
- Output speed (+15%)
GLM-5 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 GLM-5 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 (3.0× 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 GLM-5: 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).
GLM-5 — Z.ai (Zhipu) text model with an Intelligence Index of 33, a 256K-token context window and a blended price of $0.52/1M tokens (open weights).
Gemma 4 26B A4B vs GLM-5: GLM-5 scores higher on the Intelligence Index. GLM-5 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 4.0× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GLM-5 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 46 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 $0.52 per 1M tokens). Gemma 4 26B A4B is open weights and GLM-5 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 GLM-5?
Gemma 4 26B A4B takes the overall edge, though GLM-5 wins in specific areas worth weighing. GLM-5 leads overall capability (Intelligence Index 33.0 vs 25.0).
What is the main difference between the Gemma 4 26B A4B and the GLM-5?
GLM-5 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 4.0× cheaper.
Which is better value?
Gemma 4 26B A4B offers more intelligence per dollar (3.0× 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 GLM-5 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.