GPT-5.6 Sol vs GLM-5.1
Head-to-head specifications
| Metric | GPT-5.6 Sol | GLM-5.1 | Difference |
|---|---|---|---|
| Intelligence Index | 59.0 | 35.0 | +68.6% |
| Coding Index | 77.4 | 55.8 | +38.7% |
| Agentic Index | 54.0 | 29.9 | — |
| Context window | 1M tokens | 256K tokens | — |
| Blended price ($/1M tokens) | $1.54 | $0.66 | +133.3% |
| Output speed (tokens/s) | 57 | 59 | -3.4% |
| Access | Proprietary API | Open weights | — |
- GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 35.0).
- GLM-5.1 is the cheaper model to run at $0.66/1M blended tokens — about 2.3× cheaper.
- GPT-5.6 Sol offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: GPT-5.6 Sol or GLM-5.1?
GPT-5.6 Sol advantages
- General intelligence (+41%)
- Coding ability (+28%)
- Agentic task performance (+45%)
- Context window (+74%)
GLM-5.1 advantages
- Affordability (+57%)
Which should you choose?
- Choose the GPT-5.6 Sol if you need the strongest overall reasoning and accuracy.
- Choose the GLM-5.1 if you want the lowest cost per token at scale.
- Choose the GPT-5.6 Sol if coding and software development are your main workload.
Value for money
GLM-5.1 offers more intelligence per dollar (1.4× 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.
GPT-5.6 Sol vs GLM-5.1: which should you choose?
GPT-5.6 Sol — OpenAI multimodal model with an Intelligence Index of 59, a 1M-token context window and a blended price of $1.54/1M tokens.
GLM-5.1 — Z.ai (Zhipu) text model with an Intelligence Index of 35, a 256K-token context window and a blended price of $0.66/1M tokens (open weights).
GPT-5.6 Sol vs GLM-5.1: GPT-5.6 Sol scores higher on the Intelligence Index. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 35.0). GLM-5.1 is the cheaper model to run at $0.66/1M blended tokens — about 2.3× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the GPT-5.6 Sol scores 59.0 versus 35.0. For software development, the Coding Index puts GPT-5.6 Sol ahead (77.4 vs 55.8). On agentic, multi-step tool-use tasks, GPT-5.6 Sol measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The GPT-5.6 Sol accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, GLM-5.1 generates faster (59 vs 57 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, GLM-5.1 is the cheaper model to run ($0.66 vs $1.54 per 1M tokens). GPT-5.6 Sol is proprietary api and GLM-5.1 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 GPT-5.6 Sol better than the GLM-5.1?
GPT-5.6 Sol takes the overall edge, though GLM-5.1 wins in specific areas worth weighing. GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 35.0).
What is the main difference between the GPT-5.6 Sol and the GLM-5.1?
GPT-5.6 Sol leads overall capability (Intelligence Index 59.0 vs 35.0). GLM-5.1 is the cheaper model to run at $0.66/1M blended tokens — about 2.3× cheaper.
Which is better value?
GLM-5.1 offers more intelligence per dollar (1.4× 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 GPT-5.6 Sol if you need the strongest overall reasoning and accuracy. Choose the GLM-5.1 if you want the lowest cost per token at scale.
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.