Best suited for
- 01
Frequent agent loops
Use reasoning and function calls in workflows that repeatedly inspect state, choose an action and process the result.
- 02
Cost-sensitive coding
Handle frequent code generation and investigation requests with lower token rates than the full GLM 5.3 model.
- 03
Large-context processing
Keep long instructions and reference material together without splitting every request into smaller prompts.
Benchmarks
Independent model-quality evaluations from Artificial Analysis, compared with Gemini 3.6 Flash at high reasoning effort.
AA Intelligence Index v5.0 , captured 30 October 2026.
Artificial Analysis Intelligence Index
Index score- GLM 5.3 Flash28
- Gemini 3.6 FlashNot scored
Build with the Sarvam SDK
pip install --upgrade sarvamaiV2 · Sarvam SDK · Streamingimport osfrom sarvamai import SarvamAIclient = SarvamAI(api_subscription_key=os.environ["SARVAM_API_KEY"])stream = client.chat.completions_v2(model="glm5.3-flash",messages=[{"role": "user", "content": "Explain why time to first token matters for a chat application briefly."}],max_tokens=8000,stream=True,)for chunk in stream:if not chunk.choices:continuetext = chunk.choices[0].delta.contentif text:print(text, end="", flush=True)
- 01 Install the Sarvam SDK
Install
sarvamaifor Python or JavaScript. Store your key inSARVAM_API_KEY. - 02 Choose your model
Use model ID
glm5.3-flash. The SDK callshttps://api.sarvam.ai/v2for this example.V2 Chat Completions. High reasoning is the default. Reasoning tokens count toward the 8,000-token output budget.
- 03 Validate your application
Check output quality, supported parameters and tool behaviour on representative requests before moving production traffic.
Frequently Asked
Questions
Explore other models
View catalogueGLM 5.3
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DeepSeek V4.1 Flash 1017
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General-purpose open-weight model for long-document analysis and agentic workflows.
Gemma 4 31B
- Google DeepMind
- Vision
- 256K context
31B-parameter instruction-tuned model for text and image understanding, with tool calling and optional reasoning.
From model choice to production. Bring your workload, traffic expectations and latency targets. Build on Sarvam’s serving stack in India.
From model choice to production.
Bring your workload, traffic expectations and latency targets. Build on Sarvam’s serving stack in India.