Technical Specifications of GLM-5.3-Flash
| Item | GLM-5.3-Flash |
|---|---|
| Model family | GLM-5 |
| Provider | Z.ai (Zhipu AI) |
| Model type | Native multimodal Mixture-of-Experts model |
| Total parameters | 320B |
| Active parameters | 18B |
| Architecture | Hybrid sparse + linear attention |
| Additional architecture | Manifold-Constrained Hyper-Connections (mHC) |
| Input modalities | Text, image |
| Output modality | Text |
| Context window | 1M tokens |
| Maximum output | Up to 131,000 tokens on supported API gateways |
| Reasoning | Yes |
| Function calling | Yes |
| Vision | Yes |
| Open-weight license | MIT |
| Model weights | Available on Hugging Face |
| Native multimodal status | First natively multimodal model in the GLM-5 series |
| Release date | August 26, 2026 |
| Local deployment | vLLM, SGLang, TokenSpeed, KTransformers |
| Official model name | GLM-5.3-Flash |
Z.ai describes GLM-5.3-Flash as the first natively multimodal model in the GLM-5 family. It contains 320B total parameters but activates only 18B parameters per inference step. The model introduces a hybrid architecture combining sparse and linear attention and uses mHC to improve scaling efficiency.
What Is GLM-5.3-Flash?
GLM-5.3-Flash is Z.ai's efficiency-oriented member of the GLM-5 generation, designed to combine frontier-level reasoning and agentic coding capabilities with substantially lower inference cost.
Its most important distinction from a conventional "Flash" model is that Flash does not mean a small model. GLM-5.3-Flash contains 320B total parameters, but its MoE architecture activates only 18B parameters per token. This allows Z.ai to target much lower inference computation while retaining a large parameter pool for model capacity.
It is also the first GLM-5 model with native multimodal capability. The model supports visual inputs in addition to text and is positioned for coding, browser interaction, document understanding, visual coding and agentic workflows.
Z.ai says GLM-5.3-Flash was trained from a newly trained base model rather than being a simple compressed version of GLM-5.2. Its training uses a 30-trillion-token multimodal pre-training corpus, while the architecture was redesigned around capability and inference efficiency.
Main Features of GLM-5.3-Flash
- 320B MoE with 18B active parameters: GLM-5.3-Flash combines a very large total parameter capacity with a relatively small active parameter footprint, making the model substantially more efficient to serve than a dense 320B model.
- Native multimodal understanding: Unlike earlier GLM-5 models, GLM-5.3-Flash natively processes visual inputs. Its model card provides image-understanding examples and identifies the model as multimodal.
- 1M-token context: The model is designed for long-context applications involving large repositories, extensive documents, long agent trajectories and complex multi-step workflows. Cloudflare and Vercel both list a 1,048,576-token context window.
- Hybrid sparse + linear attention: GLM-5.3-Flash is the first GLM model to combine sparse attention and linear attention. Z.ai says this architecture reduces long-context serving costs while preserving precise long-context capabilities.
- Agentic coding and tool use: The model is optimized for software engineering, terminal tasks, browser interaction and tool-driven workflows. Its reported Terminal-Bench 2.1 score is 84.3.
- Open-weight deployment: The MIT-licensed weights can be deployed with Transformers, vLLM, SGLang, TokenSpeed and KTransformers, giving developers options for self-hosting and inference optimization.
Benchmark Performance of GLM-5.3-Flash
GLM-5.3-Flash has a particularly strong profile on coding and agentic benchmarks.
| Benchmark | GLM-5.3-Flash | GLM-5.2 | Notes |
|---|---|---|---|
| Terminal-Bench 2.1 | 84.3 | 81.0 | Terminal / agentic coding |
| DeepSWE v1.1 | 63.4 | 46.2 | Software engineering |
| AutomationBench | 48.8 | 26.2 | Computer-use automation |
| Toolathlon-Verified | 78.4 | 59.9 | Tool-use capability |
| NL2Repo-Bench | 56.3 | 48.9 | Natural-language-to-repository coding |
| Agent's Last Exam | 26.3 | 20.4 | Agentic reasoning |
| Humanity's Last Exam | 55.3 | — | With tools |
| GDPval-AA v2 | 1773 Elo | 1504 Elo | Productivity / knowledge work |
| OfficeQA-Pro | 62.4 | — | Multimodal productivity |
| CharXiv RQ | 89.4 | — | Multimodal reasoning |
The official Hugging Face evaluation section lists 84.3 on Terminal-Bench 2.1, 63.4 on DeepSWE and 55.3 on HLE. Z.ai's launch materials report additional results including AutomationBench, Toolathlon, NL2Repo and GDPval.
Independent Artificial Analysis currently gives GLM-5.3-Flash an Intelligence Index of 57, placing it at #3 among 108 models in its current comparison set.
These numbers should still be interpreted with benchmark-specific caveats: several results are vendor-reported, evaluation harnesses differ, and benchmark scores should not be treated as a universal ranking of model quality.
GLM-5.3-Flash vs GLM-5.3 vs GLM-5.2
| Feature | GLM-5.3-Flash | GLM-5.3 | GLM-5.2 |
|---|---|---|---|
| Model generation | GLM-5 | GLM-5 | GLM-5 |
| Positioning | Efficient multimodal / agentic model | High-end general reasoning model | Previous-generation general model |
| Native vision | Yes | No | Model-dependent |
| Total parameters | 320B | Larger flagship configuration | Large-scale model |
| Active parameters | 18B | — | — |
| Context | 1M | 200K-class deployment | 200K-class deployment |
| Hybrid sparse/linear attention | Yes | No | No |
| Agentic coding | Excellent | Excellent | Strong |
| Open weights | Yes | Depends on deployment | Depends on deployment |
| Main advantage | Capability per unit of inference compute | Maximum GLM-5 reasoning capability | Mature and lower-cost GLM generation |
The key distinction is that GLM-5.3-Flash is not simply GLM-5.3 at a smaller size. Z.ai says it starts from a newly trained base model and introduces a redesigned hybrid attention architecture, mHC and multimodal pre-training.
GLM-5.3-Flash vs DeepSeek V4 Flash — Comparison Summary
| Dimension | GLM-5.3-Flash (Z.ai / Zhipu) | DeepSeek V4 Flash (DeepSeek) | Advantage |
|---|---|---|---|
| Release Date | August 26, 2026 | Preview April 2026; major checkpoint (0731) July 31, 2026 | — |
| Total / Active Parameters | 320B / 18B | 284B / 13B | GLM slightly larger |
| Context Window | 1M tokens | 1M tokens | Tie |
| Max Output | ~131K tokens | Up to 384K tokens | DeepSeek |
| Modalities | Native multimodal (text + image + video input) | Primarily text (experimental vision variants available) | GLM |
| Architecture Highlights | Hybrid sparse + linear attention (~3× lower attention compute, ~4.4× smaller KV cache vs full GLM-5.3) | Compressed Sparse Attention (CSA) + Heavily Compressed Attention (HCA) | Each has strengths |
| License | MIT (weights on Hugging Face) | MIT (weights available) | Tie |
| Standard API Pricing ($ / 1M tokens) | Input $0.15 / Output $0.50 (cached input ~$0.03) | Common range $0.14–$0.44 input / $0.28–$1.32 output (peak/off-peak varies) | GLM cheaper |
| Launch Promo Pricing | ~$0.075 input / $0.25 output (limited-time, ~early Sep 2026) | No equivalent promotion | GLM |
| AA Intelligence Index | 57 (vendor-reported) | ~50 (independent measurement) | GLM |
| Terminal-Bench 2.1 | 84.3 | 82.7 | GLM |
| DeepSWE | 63.4 | 54.4 | GLM |
| SWE-bench Verified | Limited independent data yet | ~79% (strong independent results) | DeepSeek |
| Key Strengths | Lower price, native multimodal, leads on several agentic/coding scores, efficient long context | More mature independent validation, excellent coding/agent track record, highly efficient long-context design, strong ecosystem | — |
| Key Weaknesses | Newer release (some scores still vendor-reported); average speed | Weaker multimodal support; higher effective pricing on many routes | — |
| Best For | General use, multimodal workloads, cost-sensitive projects, balanced agent capabilities | Pure coding agents, long-context text reasoning, scenarios needing proven independent benchmarks | — |
One-sentence summary:
As of late August 2026, GLM-5.3-Flash currently leads on price, multimodal capability, and several overlapping benchmarks, offering better overall value. DeepSeek V4 Flash remains a highly reliable choice for coding-focused agents thanks to stronger independent validation and long-context efficiency. Test both on your specific workloads before deciding.
GLM-5.3-Flash Use Cases
1. Agentic software engineering
GLM-5.3-Flash is particularly well suited to coding agents that need to inspect repositories, modify multiple files, execute terminal commands, run tests and iterate on implementation.
Its 84.3 Terminal-Bench 2.1 score and 63.4 DeepSWE result demonstrate that software engineering is one of its strongest application areas.
2. Visual coding
Because GLM-5.3-Flash is natively multimodal, developers can provide screenshots, diagrams and other visual inputs alongside coding instructions. This is useful for UI implementation, visual debugging and design-to-code workflows.
3. Long-context document analysis
The 1M-token context window makes the model suitable for large technical documentation sets, repositories, lengthy reports and multi-stage agent histories.
4. Browser and computer-use agents
The model's tool-use and multimodal capabilities make it suitable for workflows involving browsers, graphical interfaces and external tools.
5. Enterprise knowledge work
GLM-5.3-Flash can process large volumes of structured and unstructured information while using tools to perform multi-step tasks, making it suitable for document analysis, research assistance and workflow automation.
6. Self-hosted AI infrastructure
The MIT license and publicly available weights make GLM-5.3-Flash attractive for organizations that require control over deployment, data processing and inference infrastructure.
GLM-5.3-Flash API Parameters
| Parameter | Description |
|---|---|
| model | Provider-specific model identifier |
| messages | Structured conversation input |
| prompt | Single prompt input on supported APIs |
| max_tokens / max_completion_tokens | Output-token limit |
| temperature | Controls sampling randomness |
| tools | Tool/function definitions |
| stream | Enables streaming output |
| reasoning | Controls reasoning effort on supported gateways |
| Image content | Supported |
| Function calling | Supported |
| Context | Up to 1M tokens |
Vercel AI Gateway currently documents a maximum of 131,000 output tokens, with reasoning tokens counted toward the output limit.
How to Use GLM-5.3-Flash API in CometAPI
Step 1: Get API Access
Log in to cometAPI. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.
Step 2: Send Requests to GLM-5.3-Flash API
Select the “GLM-5.3-Flash” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account.
Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.
Step 3: Process Responses
The API returns structured candidate responses including generated text, citations, safety metadata, and optional tool outputs. For multimodal requests, the message content can be structured with text and image inputs when the selected CometAPI endpoint exposes the model's vision capability.
The exact CometAPI endpoint, supported parameters and current availability should be taken from the live CometAPI API documentation rather than inferred from Z.ai's native API.
For CometAPI, the integration should use the model ID shown on the live CometAPI model endpoint rather than automatically copying provider-specific IDs from Z.ai, Cloudflare or Vercel.
Limitations of GLM-5.3-Flash
- Large model footprint: Although only 18B parameters are active, the released model contains roughly 321B parameters, making self-hosting substantially more demanding than deploying a conventional 7B–70B model.
- Inference speed is not necessarily "flash" in absolute terms: Artificial Analysis currently measures roughly 50 output tokens/second in its comparison environment, placing the model well below the fastest small models.
- Very long context increases infrastructure requirements: A 1M-token context is useful but can increase memory and serving complexity.
- Benchmark performance varies by task: GLM-5.3-Flash is especially strong in agentic coding and tool-use benchmarks, but no single benchmark establishes universal superiority over frontier proprietary models.
- Multimodal output is limited: The model's native multimodal capability is primarily input-side; its standard output is text rather than generated images or video.