Z.ai: GLM 5.3
z-ai/glm-5.3
Model weightsGLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves on GLM-5.2 in coding and in the balance between performance and token efficiency.
Reasoning is always on and cannot be disabled. Reasoning efforts low, high, and max are supported; max is the default.
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).
| $0.5600 | $1.8000 | $0.1000 | 0.61s | 105 tok/s | ||
| $0.6000 | $2.0000 | $0.1000 | 0.54s | 114 tok/s | ||
| $0.0699 | $1.4000 | $0.0689 | 0.38s | 78 tok/s | ||
| $0.1790 | $1.4000 | $0.1780 | 0.30s | 74 tok/s | ||
| $0.7500 | $3.0000 | $0.1900 | 0.48s | 92 tok/s | ||
| $0.0840 | $3.6000 | $0.0790 | 0.41s | 77 tok/s | ||
| $0.0840 | $3.6000 | $0.0790 | 0.42s | 75 tok/s | ||
| $1.0000 | $4.2000 | $0.1400 | 0.92s | 96 tok/s | ||
| $0.9100 | $2.8600 | $0.1690 | 0.50s | 94 tok/s | ||
| $0.0703 | $4.3843 | $0.0703 | 1.03s | 71 tok/s | ||
| $1.4000 | $4.4000 | $0.2600 | 0.26s | 125 tok/s | ||
| $0.1900 | $4.4000 | $0.1900 | 1.48s | 82 tok/s | ||
| $0.7500 | $3.0000 | $0.1200 | 0.62s | 61 tok/s | ||
| $1.4000 | $4.4000 | $0.2600 | 0.49s | 94 tok/s | ||
| $0.9500 | $3.5000 | $0.1900 | 0.38s | 85 tok/s | ||
| $1.0000 | $4.0000 | $0.1600 | 0.25s | 96 tok/s | ||
| $1.4000 | $4.4000 | $0.1400 | 0.63s | 84 tok/s | ||
60% off | $1.40$0.5586 | $4.40$1.7560 | $0.26$0.1037 | 1.31s | 54 tok/s | |
| $0.1500 | $3.0000 | $0.1200 | 0.78s | 52 tok/s | ||
| $1.4000 | $4.4000 | $0.2600 | 0.55s | 71 tok/s | ||
| $0.90$0.5625 | $4.00$2.5000 | $0.20$0.1250 | 0.69s | 78 tok/s | ||
| $0.7000 | $2.2000 | $0.1300 | 2.57s | 61 tok/s | ||
50% off | $1.40$0.7000 | $4.40$2.2000 | $0.26$0.1300 | 2.05s | 59 tok/s | |
10% off | $1.40$1.2600 | $4.40$3.9600 | $0.26$0.2340 | 0.32s | 84 tok/s | |
| $1.1450 | $3.5220 | $0.3860 | 0.81s | 71 tok/s | ||
55% off | $1.40$0.6300 | $4.40$1.9800 | $0.26$0.1170 | 1.17s | 50 tok/s | |
50% off | $1.40$0.7000 | $4.40$2.2000 | $0.26$0.1300 | 2.60s | 52 tok/s | |
| $1.4000 | $4.4000 | $0.2600 | 1.33s | 56 tok/s | ||
| $1.4000 | $4.4000 | $0.1400 | 0.94s | 80 tok/s | ||
Fast | $2.1000 | $6.6000 | $0.3900 | 0.66s | 152 tok/s | |
| $1.4000 | $4.4000 | $0.1400 | 0.67s | 63 tok/s | ||
Fast | $2.1000 | $6.6000 | $0.2600 | 1.06s | 143 tok/s | |
Fast | $2.1000 | $6.6000 | $0.2100 | 0.63s | 115 tok/s | |
20% off | $1.75$1.4000 | $5.50$4.4000 | $0.325$0.2600 | 0.73s | 100 tok/s | |
| $1.0500 | $4.2000 | $0.1680 | 1.54s | 55 tok/s | ||
| $1.4000 | $4.4000 | $0.2600 | 0.40s | 79 tok/s | ||
| $2.1000 | $6.6000 | $0.2100 | 0.53s | 118 tok/s | ||
| $1.4000 | $4.4000 | $0.2600 | 0.57s | 113 tok/s | ||
| $1.4000 | $4.4000 | $0.2600 | 1.33s | 55 tok/s | ||
50% off | $2.80$1.4000 | $8.80$4.4000 | $0.14$0.0700 | 1.11s | 48 tok/s | |
| $1.4000 | $4.4000 | $0.2600 | 1.86s | 45 tok/s | ||
| $1.6800 | $5.2800 | $0.3120 | 1.15s | 79 tok/s | ||
| $0.9800 | $3.0800 | $0.1820 | 0.49s | 104 tok/s | ||
| $1.5400 | $4.8400 | $0.1540 | 0.67s | 76 tok/s | ||
| $1.4000 | $4.4000 | $0.2600 | 0.41s | 110 tok/s | ||
| $1.1900 | $3.7400 | $0.2380 | 1.72s | 53 tok/s | ||
Prime | $2.8000 | $8.8000 | $0.5600 | 0.72s | 115 tok/s | |
| $1.4000 | $4.4000 | $0.2800 | 0.88s | 66 tok/s | ||
Not used in Standard routing:Why these endpoints are not used | ||||||
| $0.72$0.4500 | $3.20$2.0000 | $0.16$0.1000 | 2.04s | 67 tok/s | ||
- Input $0.56 / Output $1.80105 tok/s100.00% uptime
- Input $0.60 / Output $2.00114 tok/s99.94% uptime
- Input $0.0699 / Output $1.4078 tok/s100.00% uptime
- Input $0.179 / Output $1.4074 tok/s100.00% uptime
- Input $0.75 / Output $3.0092 tok/s100.00% uptime
- Input $0.084 / Output $3.6077 tok/s100.00% uptime
- Input $0.084 / Output $3.6075 tok/s99.97% uptime
- Input $1.00 / Output $4.2096 tok/s99.97% uptime
- Input $0.91 / Output $2.8694 tok/s100.00% uptime
- Input $0.0703 / Output $4.384371 tok/s100.00% uptime
- Input $1.40 / Output $4.40125 tok/s99.46% uptime
- Input $0.19 / Output $4.4082 tok/s99.93% uptime
- Input $0.75 / Output $3.0061 tok/s99.89% uptime
- Input $1.40 / Output $4.4094 tok/s99.94% uptime
Input $0.95 / Output $3.5085 tok/s99.70% uptime- Input $1.00 / Output $4.0096 tok/s99.94% uptime
- Input $1.40 / Output $4.4084 tok/s99.96% uptime
- Input $0.5586 / Output $1.75660% off54 tok/s99.07% uptime
- Input $0.15 / Output $3.0052 tok/s99.74% uptime
Input $1.40 / Output $4.4071 tok/s99.85% uptime
Input $0.5625 / Output $2.5038% off78 tok/s99.57% uptime- Input $0.70 / Output $2.2061 tok/s100.00% uptime
- Input $0.70 / Output $2.2050% off59 tok/s99.83% uptime
- Input $1.26 / Output $3.9610% off84 tok/s100.00% uptime
- Input $1.145 / Output $3.52271 tok/s100.00% uptime
- Input $0.63 / Output $1.9855% off50 tok/s100.00% uptime
- Input $0.70 / Output $2.2050% off52 tok/s100.00% uptime
- Input $1.40 / Output $4.4056 tok/s100.00% uptime
Input $1.40 / Output $4.4080 tok/s100.00% uptime
Input $2.10 / Output $6.60152 tok/s99.06% uptimeFast- Input $1.40 / Output $4.4063 tok/s100.00% uptime
- Input $2.10 / Output $6.60143 tok/s94.83% uptimeFast
- Input $2.10 / Output $6.60115 tok/s100.00% uptimeFast
- Input $1.40 / Output $4.4020% off100 tok/s99.52% uptime
- Input $1.05 / Output $4.2055 tok/s99.72% uptime
- Input $1.40 / Output $4.4079 tok/s100.00% uptime
- Input $2.10 / Output $6.60118 tok/s100.00% uptimeFast
- Input $1.40 / Output $4.40113 tok/s95.90% uptime
- Input $1.40 / Output $4.4055 tok/s93.13% uptime
- Input $1.40 / Output $4.4050% off48 tok/s100.00% uptime
- Input $1.40 / Output $4.4045 tok/s100.00% uptime
- Input $1.68 / Output $5.2879 tok/s99.68% uptime
Input $0.98 / Output $3.08104 tok/s98.11% uptime
Input $1.54 / Output $4.8476 tok/s100.00% uptime- Input $1.40 / Output $4.40110 tok/s100.00% uptime
- Input $1.19 / Output $3.7453 tok/s99.45% uptime
- Input $2.80 / Output $8.80115 tok/s100.00% uptimePrime
- Input $1.40 / Output $4.4066 tok/s100.00% uptime
- Not used in Standard routing:Why these endpoints are not used
Input $0.45 / Output $2.0067 tok/s100.00% uptimeFlex+11 more label
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 5.3 — Price history
| $0.0690 | $1.40 | 93.3% | 22.5% | ||
| $0.0703 | $4.38 | 89.2% | 1.1% | ||
| $0.0793 | $3.60 | 94.2% | 4.9% | ||
| $0.0793 | $3.60 | 94.1% | 5.3% | ||
| $0.1239 | $1.83 | 95.2% | 14.3% | ||
| $0.1278 | $3.00 | 73.9% | 0.3% | ||
| $0.1374 | $1.75 | 92.6% | 3.9% | ||
| $0.1436 | $2.00 | 91.3% | 5.7% | ||
| $0.1537 | $3.00 | 94.7% | 0.5% | ||
| $0.1680 | $2.20 | 93.3% | 0.3% | ||
| $0.1697 | $2.20 | 93.0% | 0.2% | ||
| $0.1752 | $2.52 | 90.8% | 1.0% | ||
| $0.1780 | $1.40 | 93.6% | 6.9% | ||
| $0.1844 | $2.21 | 90.5% | 0.5% | ||
| $0.1864 | $4.20 | 94.6% | 2.6% | ||
| $0.1900 | $4.40 | 82.7% | 0.4% | ||
| $0.1934 | $1.92 | 66.2% | 0.0% | ||
| $0.2240 | $3.08 | 94.7% | 0.5% | ||
| $0.2258 | $2.86 | 92.3% | 1.8% | ||
| $0.2304 | $4.40 | 92.8% | 0.2% | ||
| $0.2305 | $4.40 | 92.8% | 0.6% | ||
| $0.2316 | $3.00 | 92.6% | 4.0% | ||
| $0.2515 | $2.14 | 92.4% | 0.7% | ||
| $0.2545 | $1.98 | 73.6% | 0.5% | ||
| $0.2586 | $3.50 | 91.0% | 0.2% | ||
| $0.2913 | $6.60 | 95.7% | 0.3% | ||
| $0.2932 | $4.40 | 97.1% | 14.1% | ||
| $0.2993 | $4.40 | 82.8% | 0.0% | ||
| $0.3041 | $3.74 | 93.1% | 0.4% | ||
| $0.3262 | $4.40 | 94.2% | 0.0% | ||
| $0.3314 | $4.40 | 84.8% | 0.8% | ||
Fast | $0.3320 | $6.60 | 93.5% | 0.3% | |
| $0.3448 | $4.40 | 92.6% | 0.3% | ||
| $0.3478 | $4.84 | 86.0% | 0.1% | ||
| $0.3537 | $4.40 | 91.8% | 0.1% | ||
| $0.3600 | $4.40 | 91.2% | 0.1% | ||
| $0.3791 | $4.40 | 89.5% | 1.7% | ||
| $0.3799 | $4.20 | 76.0% | 0.0% | ||
| $0.3966 | $4.00 | 71.8% | 0.2% | ||
| $0.4449 | $4.40 | 83.8% | 1.3% | ||
| $0.4628 | $5.28 | 89.0% | 0.1% | ||
Fast | $0.5199 | $6.60 | 85.9% | 0.2% | |
| $0.5354 | $4.40 | 77.2% | 0.0% | ||
| $0.5809 | $4.40 | 71.9% | 0.1% | ||
| $0.5923 | $3.98 | 65.1% | 0.2% | ||
Fast | $0.6159 | $6.60 | 86.8% | 0.4% | |
| $0.6464 | $4.40 | 66.1% | 0.0% | ||
Prime | $0.9067 | $8.80 | 84.5% | 0.1% | |
Not used in Standard routing:Why these endpoints are not used | |||||
| $0.1626 | $2.00 | 82.1% | 0.0% | ||
- Effective in $0.0690 / Effective out $1.40Token share22.5%
- Effective in $0.0703 / Effective out $4.38Token share1.1%
- Effective in $0.0793 / Effective out $3.60Token share4.9%
- Effective in $0.0793 / Effective out $3.60Token share5.3%
- Effective in $0.1239 / Effective out $1.83Token share14.3%
- Effective in $0.1278 / Effective out $3.00Token share0.3%
- Effective in $0.1374 / Effective out $1.75Token share3.9%
- Effective in $0.1436 / Effective out $2.00Token share5.7%
- Effective in $0.1537 / Effective out $3.00Token share0.5%
- Effective in $0.1680 / Effective out $2.20Token share0.3%
- Effective in $0.1697 / Effective out $2.20Token share0.2%
- Effective in $0.1752 / Effective out $2.52Token share1.0%
- Effective in $0.1780 / Effective out $1.40Token share6.9%
- Effective in $0.1844 / Effective out $2.21Token share0.5%
- Effective in $0.1864 / Effective out $4.20Token share2.6%
- Effective in $0.1900 / Effective out $4.40Token share0.4%
- Effective in $0.1934 / Effective out $1.92Token share0.0%
- Effective in $0.2240 / Effective out $3.08Token share0.5%
- Effective in $0.2258 / Effective out $2.86Token share1.8%
- Effective in $0.2304 / Effective out $4.40Token share0.2%
- Effective in $0.2305 / Effective out $4.40Token share0.6%
- Effective in $0.2316 / Effective out $3.00Token share4.0%
- Effective in $0.2515 / Effective out $2.14Token share0.7%
- Effective in $0.2545 / Effective out $1.98Token share0.5%
- Effective in $0.2586 / Effective out $3.50Token share0.2%
- Effective in $0.2913 / Effective out $6.60FastToken share0.3%
- Effective in $0.2932 / Effective out $4.40Token share14.1%
- Effective in $0.2993 / Effective out $4.40Token share0.0%
- Effective in $0.3041 / Effective out $3.74Token share0.4%
- Effective in $0.3262 / Effective out $4.40Token share0.0%
- Effective in $0.3314 / Effective out $4.40Token share0.8%
- Effective in $0.3320 / Effective out $6.60FastToken share0.3%
- Effective in $0.3448 / Effective out $4.40Token share0.3%
- Effective in $0.3478 / Effective out $4.84Token share0.1%
- Effective in $0.3537 / Effective out $4.40Token share0.1%
- Effective in $0.3600 / Effective out $4.40Token share0.1%
- Effective in $0.3791 / Effective out $4.40Token share1.7%
- Effective in $0.3799 / Effective out $4.20Token share0.0%
- Effective in $0.3966 / Effective out $4.00Token share0.2%
- Effective in $0.4449 / Effective out $4.40Token share1.3%
- Effective in $0.4628 / Effective out $5.28Token share0.1%
- Effective in $0.5199 / Effective out $6.60FastToken share0.2%
- Effective in $0.5354 / Effective out $4.40Token share0.0%
- Effective in $0.5809 / Effective out $4.40Token share0.1%
- Effective in $0.5923 / Effective out $3.98Token share0.2%
- Effective in $0.6159 / Effective out $6.60FastToken share0.4%
- Effective in $0.6464 / Effective out $4.40Token share0.0%
- Effective in $0.9067 / Effective out $8.80PrimeToken share0.1%
- Not used in Standard routing:Why these endpoints are not used
- Effective in $0.1626 / Effective out $2.00FlexToken share0.0%
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-5.3 (Max) Intelligence Index | 31.7 |
| Artificial Analysis | GLM-5.3 (Max) Coding Index | 74.8 |
| Artificial Analysis | GLM-5.3 (Max) Agentic Index | 53.1 |
| Artificial Analysis | GLM-5.3 (Max) GPQA Diamond | 91.7% |
| Artificial Analysis | GLM-5.3 (Max) HLE | 42.3% |
| Artificial Analysis | GLM-5.3 (Max) AA-LCR | 79.7% |
| Artificial Analysis | GLM-5.3 (Max) τ-Bench Banking | 50.3% |
| Artificial Analysis | GLM-5.3 (Max) GDPval-AA | 57.7% |
| Artificial Analysis | GLM-5.3 (Max) CritPt | 19.1% |
| Artificial Analysis | GLM-5.3 (Max) SciCode | 59.0% |
| Artificial Analysis | GLM-5.3 (Max) Terminal-Bench 4.0 | 41.9% |
| Artificial Analysis | GLM-5.3 (Max) Terminal-Bench 2.1 | 83.9% |
| Artificial Analysis | GLM-5.3 (Max) AA-Omniscience Accuracy | 32.3% |
| Artificial Analysis | GLM-5.3 (Max) AA-Omniscience Non-Hallucination Rate | 61.1% |
| Artificial Analysis | GLM-5.3 (Low) Intelligence Index | 23.1 |
| Artificial Analysis | GLM-5.3 (Low) HLE | 36.6% |
| Artificial Analysis | GLM-5.3 (Low) AA-LCR | 72.7% |
| Artificial Analysis | GLM-5.3 (Low) GDPval-AA | 41.6% |
| Artificial Analysis | GLM-5.3 (Low) CritPt | 14.6% |
| Artificial Analysis | GLM-5.3 (Low) SciCode | 42.0% |
| Artificial Analysis | GLM-5.3 (Low) Terminal-Bench 4.0 | 34.8% |
| Artificial Analysis | GLM-5.3 (Low) AA-Omniscience Accuracy | 31.9% |
| Artificial Analysis | GLM-5.3 (Low) AA-Omniscience Non-Hallucination Rate | 36.8% |
| Design Arena | GLM-5.3 Models Arena 3D Elo | 1294 |
| Design Arena | GLM-5.3 Models Arena Asciiart Elo | 1198 |
| Design Arena | GLM-5.3 Models Arena Code Categories Elo | 1283 |
| Design Arena | GLM-5.3 Models Arena Data Visualization Elo | 1251 |
| Design Arena | GLM-5.3 Models Arena Game Development Elo | 1288 |
| Design Arena | GLM-5.3 Models Arena SVG Elo | 1246 |
| Design Arena | GLM-5.3 Models Arena UI Component Elo | 1285 |
| Design Arena | GLM-5.3 Models Arena Website Elo | 1279 |
| Design Arena | GLM-5.3 Agents Arena Htmlslides Elo | 1189 |
| Design Arena | GLM-5.3 Agents Arena Mobile Apps Elo | 1184 |
| Design Arena | GLM-5.3 Agents Arena Python-Pptxslides Elo | 1216 |
| Design Arena | GLM-5.3 Agents Arena Webapps Elo | 1270 |
| Vals AI | GLM 5.3 Access to Justice Benchmark | 73.7% |
| Vals AI | GLM 5.3 Code Migration v1.1 | 55.1% |
| Vals AI | GLM 5.3 CyberBench v1.1 | 72.1% |
| Vals AI | GLM 5.3 EMB v1.1 | 69.1% |
| Vals AI | GLM 5.3 Finance Agent v2.1 | 65.0% |
| Vals AI | GLM 5.3 Vals Finance Index | 61.4% |
| Vals AI | GLM 5.3 Financial Coaching Bench | 74.1% |
| Vals AI | GLM 5.3 GPQA Diamond | 88.1% |
| Vals AI | GLM 5.3 HLAB-Vals-1.1 | 14.6% |
| Vals AI | GLM 5.3 Immigration | 66.7% |
| Vals AI | GLM 5.3 IOI | 68.4% |
| Vals AI | GLM 5.3 LiveCodeBench | 80.5% |
| Vals AI | GLM 5.3 LegalBench | 84.8% |
| Vals AI | GLM 5.3 Vals Legal Index | 74.6% |
| Vals AI | GLM 5.3 Legal Research Bench | 49.0% |
| Vals AI | GLM 5.3 MedCode | 42.9% |
| Vals AI | GLM 5.3 MedScribe | 88.8% |
| Vals AI | GLM 5.3 MMLU Pro | 86.8% |
| Vals AI | GLM 5.3 MysteryMechanism | 23.0% |
| Vals AI | GLM 5.3 ProgramBench | 1.5% |
| Vals AI | GLM 5.3 ProofBench v1.1 | 49.0% |
| Vals AI | GLM 5.3 Public Benefits Bench v1.1 | 68.5% |
| Vals AI | GLM 5.3 Vals RSI Index | 22.9% |
| Vals AI | GLM 5.3 SkillsBench | 47.5% |
| Vals AI | GLM 5.3 SRE Bench | 9.2% |
| Vals AI | GLM 5.3 SWE-bench | 95.4% |
| Vals AI | GLM 5.3 Tax Agent Bench | 43.3% |
| Vals AI | GLM 5.3 TaxEval v2 | 72.4% |
| Vals AI | GLM 5.3 Terminal-Bench 4.0 | 38.9% |
| Vals AI | GLM 5.3 Terminal-Bench-Science | 5.7% |
| Vals AI | GLM 5.3 Vals Index | 59.8% |
| Vals AI | GLM 5.3 Vibe Code Bench 1-100 v1.1 | 29.8% |
| Vals AI | GLM 5.3 Vibe Code Bench v1.1 | 78.1% |
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.
Available through EU in-region routing
Z.ai: GLM 5.3 is available through EU in-region routing. Requests sent to eu.openrouter.ai are decrypted in the EU and routed only to shared provider endpoints in Europe (the EU/EEA, the UK, and Switzerland).
- Providers in Europe
- Inceptron, Mistral
- Prices in Europe
- Inceptron: $1/M tokens input, $4.20/M tokens output
- Mistral: $1.54/M tokens input, $4.84/M tokens output
- Base URL
https://eu.openrouter.ai/api/v1- Plan
- Requires Business or Enterprise. Self-serve on Business: no contract, no monthly minimum. OpenRouter Business for EU in-region routing
Fails closed: if no shared provider endpoint in Europe is available, the request errors instead of leaving the region. Requests served by your own BYOK key go to your deployment’s region, which you control. In-region routing docs
Documentation
Guides, cookbooks, blog posts, and tools for building with Z.ai: GLM 5.3 on OpenRouter.
Frequently asked questions
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves on GLM-5.2 in coding and in the balance between performance and token efficiency. Reasoning is always on and cannot be disabled. Reasoning efforts low, high, and max are supported; max is the default.
The GLM 5.3 documentation on OpenRouter includes the Self-Hosted LLM vs API: When Does Self-Hosting Pay Off blog post and the How to Benchmark LLMs blog post. The quickstart covers API keys and your first request, and the API reference lists every parameter.
GLM 5.3 costs $0.0699/M input tokens and $1.40/M output tokens, with separate rates for Cache Read at $0.0689/M tokens.
GLM 5.3 has a 1,048,576 token context window. It supports up to 1,048,576 completion tokens.
Yes. GLM 5.3 accepts tools for function calling. It also supports structured outputs via a JSON schema in response_format.
GLM 5.3 is served by 37 providers on OpenRouter: Wafer, Relace, inference.net, Reka AI, AkashML, DeepInfra, Baidu Qianfan, Decart and 29 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 5.3 was released on August 18, 2026.