GLM-5.3-Flash is a frontier-class, natively multimodal model developed by Z.ai and hosted on DeepInfra. It uses a Mixture-of-Experts (MoE) architecture with 320 billion total parameters, of which only 18 billion are active during inference.

The model is built for complex, long-horizon tasks — advanced software engineering, agentic workflows, and multimodal reasoning — with a context window of just over one million tokens (1,048,576) and a serving stack tuned for efficiency. Z.ai positions it as delivering strong intelligence at a fraction of the cost of GLM-5.2, its immediate predecessor.

Architecture and Serving Innovations

GLM-5.3-Flash introduces several changes over GLM-5.2 aimed at cutting compute without giving up reasoning quality.

• Hybrid attention: a combination of sparse and linear attention that Z.ai says cuts attention compute by 3.0x compared to GLM-5.3 (the non-Flash model in the same generation), reducing the overhead of long-context serving while preserving reasoning accuracy.
• Manifold-Constrained Hyper-Connections (mHC): paired with a 30-trillion-token multimodal pre-training corpus, mHC is designed to raise intelligence per unit of compute.
• IndexPool: compresses the model’s key vectors into a KV cache Z.ai reports as 4.4x smaller than GLM-5.3’s, supporting efficient retrieval across the full 1M-token context without a performance hit.
• Encode–Prefill–Decode (EPD) disaggregated serving: separates the three inference phases onto specialized resource pools to raise hardware utilization. Z.ai has also claimed that this lets the model reach per-token costs and efficiency comparable to mainstream NVIDIA GPUs when run on domestic accelerators — a claim worth treating as Z.ai’s own, since no independent, audited benchmark backing it has been published yet.

On DeepInfra, GLM-5.3-Flash is available with private endpoint deployment, JSON mode, and function/tool calling.

Performance Benchmarks

Z.ai positions GLM-5.3-Flash as a “Flash” model that competes with frontier-class systems — Claude Opus 4.8, GPT-5.6 Terra, and Gemini 3.7 Flash — on coding and agentic workflows, at a fraction of the parameter count active per token.

Comparison with Peer Models

The table below reflects Z.ai’s own published benchmark results for GLM-5.3-Flash.

Note: an earlier version of this table included a row labeled “OSWorld 2.0” with scores for each model. That benchmark does not appear anywhere in Z.ai’s official results for GLM-5.3-Flash, and the figures could not be verified against any source — it’s been removed and replaced with CharXiv Reasoning and Chartography, two vision benchmarks Z.ai does report.

Specialized Task Performance

• Coding & Software Engineering: On DeepSWE v1.1, GLM-5.3-Flash scores 63.4, ahead of Claude Opus 4.8 (58.0). It also includes a visual self-verification loop that lets it inspect and refine frontend layouts against a rendered screenshot.
• Agentic Capabilities: GLM-5.3-Flash scores 78.4 on Toolathlon Verified, ahead of both Claude Opus 4.8 and GPT-5.6 Terra on tool-calling accuracy.
• Multimodal Reasoning: the model scores 77.8 on MVBench (video understanding) and 89.4 on CharXiv Reasoning with tools (document analysis).

Context Window and Retrieval

• Maximum context: 1,048,576 tokens (~1M).
• KV cache: IndexPool compresses key vectors to a cache Z.ai reports as 4.4x smaller than GLM-5.3’s, supporting long-context retrieval without a measurable quality drop.

Getting Started with the API

DeepInfra provides an OpenAI-compatible interface for GLM-5.3-Flash, so it drops into existing OpenAI-client code with a base URL and model-name change.

Authentication

Use an API key from your DeepInfra dashboard.

• Header: Authorization
• Format: Bearer <YOUR_DEEPINFRA_API_KEY>

API Endpoint Details

• Base URL: https://api.deepinfra.com/v1/openai
• Endpoint: /chat/completions
• Method: POST
• Model identifier: zai-org/GLM-5.3-Flash

Implementation Example

curl https://api.deepinfra.com/v1/openai/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPINFRA_API_KEY" \
  -d '{
    "model": "zai-org/GLM-5.3-Flash",
    "messages": [
      {
        "role": "user",
        "content": "Write a Python function to find the greatest common divisor (GCD) of two numbers."
      }
    ]
  }'
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Common Parameters

See DeepInfra’s full API docs for streaming, vision inputs, and additional parameters.

Pricing and Cost Efficiency

Z.ai positions GLM-5.3-Flash as delivering high-tier intelligence at roughly one-tenth the cost of GLM-5.2. DeepInfra is currently running a 50% promotional discount on standard rates.

Token Pricing (per 1 Million Tokens)

See the pricing guide for a full breakdown across scenarios and providers.

Key Considerations

• Usage-based billing: costs scale with the volume of tokens processed.
• Cached tokens: significantly reduced rates for cached input make repeated long-context queries cheaper.
• Private endpoints: organizations needing dedicated compute can deploy a private endpoint from the DeepInfra dashboard.

Conclusion

GLM-5.3-Flash pairs a 1M-token context window with an efficiency-focused MoE architecture, giving developers frontier-level coding, agentic, and multimodal reasoning capability at a fraction of typical cost. Whether you’re building autonomous software agents, analyzing visual documents, or processing large datasets, it’s available now on DeepInfra with an OpenAI-compatible API. See also: GLM-5.3-Flash Is Now on DeepInfra and the pricing and cost analysis.