Z.ai: GLM 5.3 Flash (batch)
z-ai/glm-5.3-flash:batch
Model weightsGLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while reducing compute overhead.
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).
| $0.050 | $0.18 | $0.010 | -- | -- | ||
| $0.075 | $0.25 | $0.015 | -- | -- | ||
| $0.120 | $0.40 | $0.024 | -- | -- |
- Input $0.05 / Output $0.18-- tok/s-- uptime
- Input $0.075 / Output $0.25-- tok/s-- uptime
Input $0.12 / Output $0.40-- tok/s-- 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 5.3 Flash (batch) — Price history
| $0.00 | $0.00 | 7.8% | 99.6% | ||
| $0.00 | $0.00 | 6.7% | 0.1% | ||
| $0.00 | $0.00 | 0.0% | 0.2% |
- Effective in $0.00 / Effective out $0.00Token share99.6%
- Effective in $0.00 / Effective out $0.00Token share0.1%
- Effective in $0.00 / Effective out $0.00Token share0.2%
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).
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.
Not enough uptime data to display yet.
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-Flash Intelligence Index | 27.8 |
| Artificial Analysis | GLM-5.3-Flash Coding Index | 71.5 |
| Artificial Analysis | GLM-5.3-Flash Agentic Index | 50.9 |
| Artificial Analysis | GLM-5.3-Flash GPQA Diamond | 91.2% |
| Artificial Analysis | GLM-5.3-Flash HLE | 39.9% |
| Artificial Analysis | GLM-5.3-Flash AA-LCR | 80.0% |
| Artificial Analysis | GLM-5.3-Flash τ-Bench Banking | 47.2% |
| Artificial Analysis | GLM-5.3-Flash GDPval-AA | 57.4% |
| Artificial Analysis | GLM-5.3-Flash CritPt | 15.4% |
| Artificial Analysis | GLM-5.3-Flash SciCode | 51.6% |
| Artificial Analysis | GLM-5.3-Flash Terminal-Bench 4.0 | 32.8% |
| Artificial Analysis | GLM-5.3-Flash Terminal-Bench 2.1 | 84.3% |
| Artificial Analysis | GLM-5.3-Flash AA-Omniscience Accuracy | 28.2% |
| Artificial Analysis | GLM-5.3-Flash AA-Omniscience Non-Hallucination Rate | 61.7% |
| Design Arena | GLM-5.3-Flash Models Arena 3D Elo | 1293 |
| Design Arena | GLM-5.3-Flash Models Arena Asciiart Elo | 1269 |
| Design Arena | GLM-5.3-Flash Models Arena Code Categories Elo | 1267 |
| Design Arena | GLM-5.3-Flash Models Arena Data Visualization Elo | 1258 |
| Design Arena | GLM-5.3-Flash Models Arena Game Development Elo | 1266 |
| Design Arena | GLM-5.3-Flash Models Arena SVG Elo | 1271 |
| Design Arena | GLM-5.3-Flash Models Arena UI Component Elo | 1296 |
| Design Arena | GLM-5.3-Flash Models Arena Website Elo | 1257 |
| Vals AI | GLM 5.3 Flash Access to Justice Benchmark | 71.1% |
| Vals AI | GLM 5.3 Flash Code Migration v1.1 | 51.6% |
| Vals AI | GLM 5.3 Flash CyberBench v1.1 | 67.9% |
| Vals AI | GLM 5.3 Flash CyberOffense | 18.3% |
| Vals AI | GLM 5.3 Flash EMB v1.1 | 66.3% |
| Vals AI | GLM 5.3 Flash Finance Agent v2.1 | 60.6% |
| Vals AI | GLM 5.3 Flash Vals Finance Index | 55.8% |
| Vals AI | GLM 5.3 Flash GPQA Diamond | 86.4% |
| Vals AI | GLM 5.3 Flash HLAB-Vals-1.1 | 10.4% |
| Vals AI | GLM 5.3 Flash IOI | 52.5% |
| Vals AI | GLM 5.3 Flash LiveCodeBench | 80.5% |
| Vals AI | GLM 5.3 Flash LegalBench | 83.9% |
| Vals AI | GLM 5.3 Flash Vals Legal Index | 71.1% |
| Vals AI | GLM 5.3 Flash Legal Research Bench | 45.2% |
| Vals AI | GLM 5.3 Flash MedScribe | 88.9% |
| Vals AI | GLM 5.3 Flash MMLU Pro | 86.1% |
| Vals AI | GLM 5.3 Flash MMMU Pro | 86.0% |
| Vals AI | GLM 5.3 Flash ProgramBench | 0.0% |
| Vals AI | GLM 5.3 Flash ProofBench v1.1 | 21.0% |
| Vals AI | GLM 5.3 Flash SAGE | 89.3% |
| Vals AI | GLM 5.3 Flash SkillsBench | 40.2% |
| Vals AI | GLM 5.3 Flash SRE Bench | 0.8% |
| Vals AI | GLM 5.3 Flash SWE-bench | 92.0% |
| Vals AI | GLM 5.3 Flash Tax Agent Bench | 31.6% |
| Vals AI | GLM 5.3 Flash TaxEval v2 | 75.6% |
| Vals AI | GLM 5.3 Flash Terminal-Bench 4.0 | 37.4% |
| Vals AI | GLM 5.3 Flash Terminal-Bench-Science | 2.9% |
| Vals AI | GLM 5.3 Flash Vals Index | 50.9% |
| Vals AI | GLM 5.3 Flash Vibe Code Bench 1-100 | 16.0% |
| Vals AI | GLM 5.3 Flash Vibe Code Bench v1.1 | 30.8% |
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.
Not enough data for apps using this model yet.
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
Documentation
Guides, cookbooks, blog posts, and tools for building with Z.ai: GLM 5.3 Flash (batch) on OpenRouter.
Frequently asked questions
Yes, the stealth model Ox Alpha was revealed to be ZAI's new model, GLM-5.3 Flash.
GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while reducing compute overhead.
The GLM 5.3 Flash (batch) documentation on OpenRouter includes the Open-Weight vs Closed-Weight Models: What's the Difference? blog post. The quickstart covers API keys and your first request, and the API reference lists every parameter.
GLM 5.3 Flash (batch) costs $0.05/M input tokens and $0.18/M output tokens, with separate rates for Cache Read at $0.01/M tokens.
GLM 5.3 Flash (batch) has a 1,048,576 token context window. It supports up to 131,072 completion tokens.
Yes. GLM 5.3 Flash (batch) accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
GLM 5.3 Flash (batch) accepts text, images, and video as input and returns text.
GLM 5.3 Flash (batch) is served by 3 providers on OpenRouter: Sail Research, Together, and DeepInfra. 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 Flash (batch) was released on August 26, 2026.