LiteLLM vs Langfuse
Six of fourteen models named LiteLLM first on the direct prompt; zero named Langfuse. LiteLLM was named by fourteen of the fourteen models and Langfuse by thirteen and LiteLLM carries 66 labels and Langfuse 16, so the shares are not directly comparable.
Named in three categories this edition.
Named in four categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the LLM gateways and observability page.
By framing
LiteLLMFirst choices, of fourteen modelsLangfuse
Across every category in the November 2026 Edition, LiteLLM and Langfuse were named in the same answer twenty-eight times, of the 207 answers naming LiteLLM and the 54 naming Langfuse. In those answers Langfuse took the first choice two times and LiteLLM six.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | | | | ||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | | | | |
| Grok 4.20 | | | | | | |
| Mistral Small | | | | | ||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | ||||
| Qwen 3.7 Flash | | | | | | |
| Kimi K2 | | | | | ||
| GLM 4.7 FlashX | | | | | | |
| MiniMax M2.5 | | | | | ||
| GPT-6 Luna | | | | | ||
| Muse Glimmer 30B | | | | | |
LiteLLM Langfuse first choice named as an alternative argued againstblank: not namedEach cell is one answer, LiteLLM on the left and Langfuse on the right.The direct prompt
LiteLLM first, Langfuse not the choice
6 of 14 modelsLangfuse was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
LiteLLM36%#1 of 9Langfuse2%#7 of 9What the models said about LiteLLM
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“"LiteLLM put a credential stealer on PyPI and Docker on the day of my test, its second compromised release this year." [6]” Muse Glimmer 30B · negative prompt · hard negative
“you should be especially cautious about—or avoid—self-hosted or open-source LLM gateways like LiteLLM and Ollama” Mistral Small · negative prompt · hard negative
“The clearest current red flag is LiteLLM’s 2026 package/container compromises; avoid unverified versions” Perplexity Sonar · negative prompt · hard negative
“The de facto open-source standard for multi-provider integration... Choose LiteLLM if you want a flexible, open-source proxy to eliminate vendor lock-in” Gemini 3.5 Flash · comparative prompt · first choice
“Best overall for a limited budget: Self‑host LiteLLM (or Bifrost) → pay only provider rates, full control, and strong cost governance.” GLM 4.7 FlashX · budget prompt · first choice
“Choose LiteLLM if you are a Python shop, need to self-host for data compliance, or want granular control over complex routing rules.” Qwen 3.7 Flash · comparative prompt · first choice
What the models said about Langfuse
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.
“Top Recommendation: Langfuse + Bifrost” GLM 4.7 FlashX · paraphrase prompt · first choice
“Langfuse (top if you prioritize open-source/self-hosting): Excellent framework-agnostic tracing” Grok 4.20 · paraphrase prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.