Z.ai: GLM 5.1
z-ai/glm-5.1
Model weightsGLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interactions, GLM-5.1 can work independently and continuously on a single task for more than 8 hours, autonomously planning, executing, and improving itself throughout the process, ultimately delivering complete, engineering-grade results.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick: Balanced (price + speed), Nitro (fastest), or Floor (cheapest).
| $1.4000 | $4.400 | $0.2600 | 0.37s | 99 tok/s | ||
36% off | $1.40$0.8988 | $4.40$2.825 | $0.26$0.1669 | 0.87s | 62 tok/s | |
| $1.2600 | $3.960 | $0.2340 | 1.64s | 82 tok/s | ||
| $0.9000 | $2.900 | $0.0900 | 1.58s | 39 tok/s | ||
| $1.3300 | $4.180 | $0.2470 | 0.90s | 86 tok/s | ||
| $1.3800 | $4.400 | $0.2600 | 3.69s | 34 tok/s | ||
9% off | $1.54$1.4010 | $4.84$4.404 | $0.286$0.2603 | 0.97s | 77 tok/s | |
| $1.4000 | $4.400 | $0.2600 | 1.96s | 58 tok/s | ||
31% off | $1.40$0.9660 | $4.40$3.036 | $0.26$0.1794 | 2.70s | 29 tok/s | |
| $1.4000 | $4.400 | $0.2600 | 2.11s | 36 tok/s | ||
| $1.1900 | $3.740 | $0.6000 | 1.19s | 65 tok/s |
- Input $1.40 / Output $4.4099 tok/s100.00% uptime
- Input $0.8988 / Output $2.82536% off62 tok/s99.81% uptime
- Input $1.26 / Output $3.9682 tok/s99.83% uptime
- Input $0.90 / Output $2.9039 tok/s99.57% uptime
- Input $1.33 / Output $4.1886 tok/s98.34% uptime
- Input $1.38 / Output $4.4034 tok/s100.00% uptime
- Input $1.401 / Output $4.4049% off77 tok/s98.12% uptime
- Input $1.40 / Output $4.4058 tok/s100.00% uptime
- Input $0.966 / Output $3.03631% off29 tok/s99.87% uptime
- Input $1.40 / Output $4.4036 tok/s100.00% uptime
- Input $1.19 / Output $3.7465 tok/s99.71% uptime
Pricing
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Weighted average
GLM 5.1 — Price history
| $0.366 | $3.04 | 76.2% | 7.9% | ||
| $0.396 | $2.82 | 68.7% | 13.6% | ||
| $0.409 | $4.40 | 86.9% | 27.8% | ||
| $0.464 | $3.96 | 77.6% | 3.6% | ||
| $0.526 | $4.40 | 76.7% | 16.7% | ||
| $0.530 | $4.40 | 75.9% | 2.6% | ||
| $0.593 | $4.40 | 70.8% | 0.9% | ||
| $0.613 | $2.90 | 35.5% | 2.1% | ||
| $0.705 | $4.40 | 61.0% | 1.7% | ||
| $0.768 | $3.74 | 71.4% | 22.6% | ||
| $0.817 | $4.18 | 47.4% | 0.5% |
- Effective in $0.366 / Effective out $3.04Token share7.9%
- Effective in $0.396 / Effective out $2.82Token share13.6%
- Effective in $0.409 / Effective out $4.40Token share27.8%
- Effective in $0.464 / Effective out $3.96Token share3.6%
- Effective in $0.526 / Effective out $4.40Token share16.7%
- Effective in $0.530 / Effective out $4.40Token share2.6%
- Effective in $0.593 / Effective out $4.40Token share0.9%
- Effective in $0.613 / Effective out $2.90Token share2.1%
- Effective in $0.705 / Effective out $4.40Token share1.7%
- Effective in $0.768 / Effective out $3.74Token share22.6%
- Effective in $0.817 / Effective out $4.18Token share0.5%
Performance
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
P50, best across providers
P50, best provider
Uptime
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Availability over the last 3 days
Last 72 hoursAvailability over the last 24 hours
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.
Benchmarks
Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.
| Source | Benchmark | Score |
|---|---|---|
| Artificial Analysis | GLM-5.1 (Reasoning) Coding Index | 55.8 |
| Artificial Analysis | GLM-5.1 (Reasoning) Agentic Index | 23.9 |
| Artificial Analysis | GLM-5.1 (Reasoning) GPQA Diamond | 86.8% |
| Artificial Analysis | GLM-5.1 (Reasoning) HLE | 30.1% |
| Artificial Analysis | GLM-5.1 (Reasoning) IFBench | 76.3% |
| Artificial Analysis | GLM-5.1 (Reasoning) τ²-Bench Telecom | 97.7% |
| Artificial Analysis | GLM-5.1 (Reasoning) AA-LCR | 73.7% |
| Artificial Analysis | GLM-5.1 (Reasoning) τ-Bench Banking | 13.6% |
| Artificial Analysis | GLM-5.1 (Reasoning) GDPval-AA | 31.2% |
| Artificial Analysis | GLM-5.1 (Reasoning) CritPt | 4.6% |
| Artificial Analysis | GLM-5.1 (Reasoning) SciCode | 44.8% |
| Artificial Analysis | GLM-5.1 (Reasoning) Terminal-Bench Hard | 43.2% |
| Artificial Analysis | GLM-5.1 (Reasoning) Terminal-Bench 4.0 | 2.0% |
| Artificial Analysis | GLM-5.1 (Reasoning) Terminal-Bench 2.1 | 61.8% |
| Artificial Analysis | GLM-5.1 (Non-reasoning) GPQA Diamond | 83.9% |
| Artificial Analysis | GLM-5.1 (Non-reasoning) HLE | 27.9% |
| Artificial Analysis | GLM-5.1 (Non-reasoning) IFBench | 52.0% |
| Artificial Analysis | GLM-5.1 (Non-reasoning) τ²-Bench Telecom | 97.1% |
| Artificial Analysis | GLM-5.1 (Non-reasoning) AA-LCR | 53.3% |
| Artificial Analysis | GLM-5.1 (Non-reasoning) CritPt | 0.0% |
| Artificial Analysis | GLM-5.1 (Non-reasoning) Terminal-Bench Hard | 35.6% |
| Design Arena | GLM 5.1 Models Arena 3D Elo | 1231 |
| Design Arena | GLM 5.1 Models Arena Asciiart Elo | 1140 |
| Design Arena | GLM 5.1 Models Arena Code Categories Elo | 1260 |
| Design Arena | GLM 5.1 Models Arena Data Visualization Elo | 1257 |
| Design Arena | GLM 5.1 Models Arena Game Development Elo | 1255 |
| Design Arena | GLM 5.1 Models Arena SVG Elo | 1207 |
| Design Arena | GLM 5.1 Models Arena UI Component Elo | 1258 |
| Design Arena | GLM 5.1 Models Arena Website Elo | 1261 |
| Design Arena | GLM 5.1 Agents Arena Agenticgamedev Elo | 1106 |
| Design Arena | GLM 5.1 Agents Arena Agentichtmlslides Elo | 1205 |
| Design Arena | GLM 5.1 Agents Arena Agenticslides Elo | 1245 |
| Design Arena | GLM 5.1 Agents Arena Agenticslides(Html) Elo | 1204 |
| Design Arena | GLM 5.1 Agents Arena Agenticslides(Python-Pptx) Elo | 1240 |
| Design Arena | GLM 5.1 Agents Arena Androidnative Elo | 1174 |
| Design Arena | GLM 5.1 Agents Arena Full Stack Elo | 1139 |
| Design Arena | GLM 5.1 Agents Arena Godotgamedev Elo | 1036 |
| Design Arena | GLM 5.1 Agents Arena Htmlslides Elo | 1065 |
| Design Arena | GLM 5.1 Agents Arena Mobile Apps Elo | 1153 |
| Design Arena | GLM 5.1 Agents Arena Pdf Elo | 1197 |
| Design Arena | GLM 5.1 Agents Arena Pptxslides Elo | 1241 |
| Design Arena | GLM 5.1 Agents Arena Python-Pptxslides Elo | 1258 |
| Design Arena | GLM 5.1 Agents Arena Python-Pptxslides Elo | 1155 |
| Design Arena | GLM 5.1 Agents Arena Spreadsheet Elo | 1315 |
| Design Arena | GLM 5.1 Agents Arena Webapps Elo | 1153 |
| Design Arena | GLM 5.1 Models Arena 3D Elo | 1336 |
| Design Arena | GLM 5.1 Models Arena Data Visualization Elo | 1366 |
| Design Arena | GLM 5.1 Models Arena UI Component Elo | 1292 |
| Design Arena | GLM 5.1 Models Arena Website Elo | 1290 |
| Vals AI | GLM 5.1 AIME | 91.9% |
| Vals AI | GLM 5.1 CaseLaw v2 | 51.6% |
| Vals AI | GLM 5.1 Code Migration | 25.8% |
| Vals AI | GLM 5.1 CorpFin v2 | 64.5% |
| Vals AI | GLM 5.1 Finance Agent (v2) | 44.8% |
| Vals AI | GLM 5.1 GPQA Diamond | 84.5% |
| Vals AI | GLM 5.1 Harvey's Legal Agent Benchmark v1 | 0.0% |
| Vals AI | GLM 5.1 LiveCodeBench | 81.4% |
| Vals AI | GLM 5.1 LegalBench | 84.4% |
| Vals AI | GLM 5.1 Legal Research Bench | 27.9% |
| Vals AI | GLM 5.1 MedCode | 41.6% |
| Vals AI | GLM 5.1 MedScribe | 72.3% |
| Vals AI | GLM 5.1 MMLU Pro | 86.9% |
| Vals AI | GLM 5.1 ProgramBench | 0.0% |
| Vals AI | GLM 5.1 ProofBench v1 | 22.2% |
| Vals AI | GLM 5.1 Public Benefits Bench v1.1 | 61.8% |
| Vals AI | GLM 5.1 SWE-bench | 76.4% |
| Vals AI | GLM 5.1 TaxEval v2 | 71.2% |
| Vals AI | GLM 5.1 Terminal-Bench 2.1 | 56.9% |
| Vals AI | GLM 5.1 Vals Index | 52.5% |
| Vals AI | GLM 5.1 Vibe Code Bench v1.1 | 31.5% |
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Activity
Token volume and request traffic to this model over time.
API
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
Not currently available through EU in-region routing. In-region routing docs
Frequently asked questions
GLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interactions, GLM-5.1 can work independently and continuously on a single task for more than 8 hours, autonomously planning, executing, and improving itself throughout the process, ultimately delivering complete,...
GLM 5.1 costs $0.8988/M input tokens and $2.825/M output tokens, with separate rates for Cache Read at $0.1669/M tokens.
GLM 5.1 has a 204,800 token context window. It supports up to 131,072 completion tokens.
Yes. GLM 5.1 accepts tools and tool_choice for function calling on 10 of the 11 providers serving it, and requests that send tools are routed to those providers. It also supports structured outputs via a JSON schema in response_format.
GLM 5.1 is served by 11 providers on OpenRouter: Baidu Qianfan, Chutes, StreamLake, SiliconFlow, AtlasCloud, Alibaba Cloud Int., NovitaAI, GMICloud and 3 more. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.
GLM 5.1 was released on April 7, 2026.