Best suited for
- 01
Repository-scale coding
Keep implementation details, dependencies and test output in context while an agent investigates issues and prepares changes.
- 02
Long-horizon tool use
Combine reasoning with function calls to work through tasks that require several decisions and interactions with your systems.
- 03
Complex document reasoning
Bring instructions and source material into a large context window for synthesis, analysis and questions across documents.
Benchmarks
Artificial Analysis evaluates GLM 5.3 at max reasoning effort. Compare model quality with the named closed-model configurations below.
AA Intelligence Index v5.0 , captured 30 October 2026.
Artificial Analysis Intelligence Index
Index score- GLM 5.332
- GPT-5.6 TerraNot 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",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. The SDK callshttps://api.sarvam.ai/v2for this example.V2 Chat Completions. Max 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 Flash
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- Reasoning and coding
- 1M context
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DeepSeek V4.1 Flash 1017
- DeepSeek
- Reasoning and coding
- 1M context
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.