Comparison
litellm vs MInference
Verdict
Pick litellm if litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging; pick MInference if mInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy.
Markdown twin · litellm alternatives · MInference alternatives
GraphCanon updated today
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Trust & integrity
| Signal | litellm | MInference |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Slowing (94d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of 1d · github_public_v1 |
| Security (OSV) | 2 low (2 low) As of 1d · osv@v1 | No lockfile As of 1d · none |
Tagline
- litellm
- Python SDK and Proxy Server for calling multiple LLM APIs
- MInference
- Accelerates Long-context LLMs' inference through approximate sparse calculation for attention.
Stars
- litellm
- 53k
- MInference
- 1.2k
Forks
- litellm
- 9.7k
- MInference
- 78
Open issues
- litellm
- 3.9k
- MInference
- 93
Language
- litellm
- Python
- MInference
- Python
Adopt for
- litellm
- litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging.
- MInference
- MInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy.
Persona
- litellm
- -
- MInference
- -
Runtime
- litellm
- -
- MInference
- -
License
- litellm
- The licensing terms for LiteLLM are provided under a license type categorized as 'Other'; details of the exact license should be referenced directly from its source.
- MInference
- MIT
Last pushed
- litellm
- Jul 11, 2026
- MInference
- Apr 8, 2026
Categories
- litellm
- Inference & Serving, LLM Frameworks
- MInference
- Inference & Serving
Trust and health
Maintenance
- litellm
- Very active (96%)
- MInference
- Slowing (36%)
Days since push
- litellm
- 0d
- MInference
- 94d
Open issues (now)
- litellm
- 3.9k
- MInference
- 93
Security scan
- litellm
- 2 low (2 low)
- MInference
- No lockfile
Full report
- litellm
- Trust report
- MInference
- Trust report
Choose litellm if…
- License: litellm is Other, MInference is MIT.
- Pricing: While the core functionality is provided free, specific extended features might require a paid plan..
- Requirements: Requires Docker.
- Tags unique to litellm: ai-gateway, azure-openai, bedrock, llm.
- Also covers LLM Frameworks.
- litellm ships Docker support for self-hosted deployment.
- When you need to integrate multiple LLM (Language Learning Modelling) APIs into your application across different providers like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, Hugging
When NOT to use litellm
- If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill.
Choose MInference if…
- License: MInference is MIT, litellm is Other.
- Requirements: Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration..
- Tags unique to MInference: attention mechanism, flashattention-2, inference acceleration, long-context llms.
- MInference is ideal for scenarios where significant reduction in inference latency is needed without sacrificing the accuracy of long-context LLM outputs.
When NOT to use MInference
- Avoid using MInference if your application does not benefit from or cannot tolerate slight variations in inference times due to its use of approximate sparse calculation.
- MInference might not be suitable for applications where the model's accuracy is critical and any reduction in the precision introduced by approximations would be detrimental.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (BerriAI/litellm) · observed Jul 11, 2026
- GitHub forks (BerriAI/litellm) · observed Jul 11, 2026
- Last push (BerriAI/litellm) · observed Jul 11, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (microsoft/MInference) · observed Jul 11, 2026
- GitHub forks (microsoft/MInference) · observed Jul 11, 2026
- Last push (microsoft/MInference) · observed Apr 8, 2026
- License file (MIT) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: litellm 53k · MInference 1.2k (synced Jul 11, 2026).
Common questions
- What is the difference between litellm and MInference?
- litellm: Python SDK and Proxy Server for calling multiple LLM APIs. MInference: Accelerates Long-context LLMs' inference through approximate sparse calculation for attention.. See the comparison table for live GitHub stats and shared categories.
- When should I choose litellm over MInference?
- Choose litellm over MInference when License: litellm is Other, MInference is MIT; Pricing: While the core functionality is provided free, specific extended features might require a paid plan.; Requirements: Requires Docker; Tags unique to litellm: ai-gateway, azure-openai, bedrock, llm; Also covers LLM Frameworks; litellm ships Docker support for self-hosted deployment; When you need to integrate multiple LLM (Language Learning Modelling) APIs into your application across different providers like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, Hugging.
- When should I choose MInference over litellm?
- Choose MInference over litellm when License: MInference is MIT, litellm is Other; Requirements: Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration.; Tags unique to MInference: attention mechanism, flashattention-2, inference acceleration, long-context llms; MInference is ideal for scenarios where significant reduction in inference latency is needed without sacrificing the accuracy of long-context LLM outputs.
- When should I avoid litellm?
- If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill.
- When should I avoid MInference?
- Avoid using MInference if your application does not benefit from or cannot tolerate slight variations in inference times due to its use of approximate sparse calculation. MInference might not be suitable for applications where the model's accuracy is critical and any reduction in the precision introduced by approximations would be detrimental.
- Is litellm or MInference more popular on GitHub?
- litellm has more GitHub stars (53,271 vs 1,221). Stars measure visibility, not whether either tool fits your constraints.
- Are litellm and MInference open source?
- Yes - both are open-source projects on GitHub (litellm: Other, MInference: MIT).
- Where can I find alternatives to litellm or MInference?
- GraphCanon lists graph-backed alternatives at litellm alternatives and MInference alternatives (litellm markdown twin, MInference markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, litellm or MInference?
- litellm: Very active. MInference: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for litellm and MInference?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: litellm trust report; MInference trust report.