LLM API Parameter Comparison Across OpenAI, Claude, Gemini, DeepSeek, Kimi, MiniMax, Yi

Core Issue

When switching between LLM providers that claim “OpenAI‑compatible” APIs, most failures come from parameter mismatches rather than SDK bugs. The same field name can have different value ranges, be immutable on some models, or be unsupported entirely.

Five Types of Parameter Pitfalls

Different value ranges for the same name – e.g., temperature is 0‑2 on OpenAI, DeepSeek, MiniMax, Yi but 0‑1 on Kimi and Claude.

Missing parameters – top_k, presence_penalty, frequency_penalty, seed, logprobs are absent on Claude and Gemini.

Locked parameters – Kimi‑k2.5 fixes temperature and top_p; some DeepSeek models lock max_tokens.

Incompatible parameter combinations – recent Claude versions forbid sending temperature and top_p together.

Protocol‑only compatibility – “OpenAI compatible” often means the request shape matches, not that the generation behavior is identical.

Practical Implications

Treat temperature as a relative value within each platform. A value of 1.0 may be near the upper bound on Claude/Kimi but mid‑range on OpenAI/DeepSeek.

Parameter Availability Overview

Supported by most platforms : temperature, top_p, max_tokens, logprobs, tools, response_format, stream, stop.

Only on some platforms : top_k (DeepSeek, Kimi, MiniMax, Yi, Gemini), presence_penalty / frequency_penalty (OpenAI, DeepSeek, Kimi, MiniMax, Yi), repetition_penalty (Yi), seed (OpenAI, DeepSeek, MiniMax, Yi, Gemini).

Compatibility Levels (Protocol → Parameter → Behavior)

★★★★★ DeepSeek – endpoint /chat/completions, same field names, identical ranges, works with OpenAI SDK out of the box.

★★★★☆ Kimi – same endpoint, but temperature range is 0‑1 and k2.5 models lock several fields.

★★★☆☆ MiniMax – different endpoint ( /v1/text/chatcompletion_v2) but otherwise similar parameter set.

★★★★☆ Yi (DashScope) – OpenAI‑style request format, adds repetition_penalty for token‑level repetition control.

★★☆☆☆ Claude – distinct /messages API, different defaults, and disallows some parameter combos.

★★☆☆☆ Gemini – completely different API ( /models/{model}:generateContent) and naming; many parameters have different defaults.

Decision Flow for Model Selection

1. Identify hard requirements
   ├─ Multimodal? → Kimi k2.5 / Gemini
   ├─ Context >200K tokens? → MiniMax / Claude
   ├─ Need token‑level probabilities? → DeepSeek / OpenAI / MiniMax
   └─ Cost‑sensitive? → DeepSeek / Yi

2. Check parameter needs
   ├─ Need <code>top_k</code>? → Exclude OpenAI
   ├─ Need <code>presence_penalty</code>/<code>frequency_penalty</code>? → Exclude Claude & Gemini
   ├─ Need <code>temperature</code> + <code>top_p</code> together? → Exclude newer Claude versions
   └─ Need reproducible output (<code>seed</code>)? → Prefer OpenAI or Gemini

3. Migration effort
   ├─ Minimal code changes → DeepSeek / Kimi / Yi
   └─ Will accept a dedicated adapter layer → Claude / Gemini

Why an Adapter Layer Is Essential

When you operate multiple providers (model switching, A/B testing, cost routing, failover, or mixing domestic and international services), a unified gateway abstracts endpoint differences, normalises parameters, and centralises governance (rate limiting, budgeting, key management, observability). The layer does not eliminate differences; it concentrates translation logic, fallback policies, and policy enforcement in one place.

Python Ecosystem

LiteLLM – lightweight unified adapter and proxy (32k+ ★ on GitHub). Supports 100+ models, works in SDK mode ( completion(...)) or as an HTTP proxy.

LangChain – full‑stack LLM framework (100k+ ★). Provides Chains, Agents, Memory, RAG on top of the same model adapters.

AiSuite – minimal wrapper (12k+ ★). One‑line call using OpenAI‑style syntax.

JavaScript / TypeScript Ecosystem

Vercel AI SDK – front‑end‑first SDK with excellent streaming support (10k+ ★).

Genkit – Google’s full‑stack AI framework (5k+ ★) supporting JS/TS, Go, Python, Dart.

LangChain.js – JavaScript port of LangChain (15k+ ★) for complex agents.

pi‑ai – lightweight TypeScript adapter with strong type safety.

Go Ecosystem

LangChain Go – Go implementation of Chains, Memory, Tools.

Genkit Go SDK – mirrors the multi‑language Genkit experience.

Java / JVM Ecosystem

Spring AI – integrates LLMs into Spring Boot, exposing unified Chat, Embedding, and Vector Store abstractions.

LangChain4j – Java version of LangChain, supporting Chains and Agents.

Self‑Hosted Gateway Options

LiteLLM Proxy – Docker‑based OpenAI‑style proxy (32k+ ★). Example docker‑compose snippet:

services:
  litellm:
    image: ghcr.io/berriai/litellm:main-latest
    ports: ["4000:4000"]
    environment:
      OPENAI_API_KEY: "your-key"
      ANTHROPIC_API_KEY: "your-key"
      DEEPSEEK_API_KEY: "your-key"

All clients can point to http://localhost:4000 and use the standard OpenAI request format.

OpenRouter – hosted multi‑model gateway with routing and cost control.

One API / New API – Chinese‑market gateway that normalises domestic providers and offers virtual keys and quota management.

LLM Gateway (open‑source) – fully OpenAI‑style gateway for any language.

When to Choose Which Provider

Deterministic output (code, math, factual QA) – DeepSeek or OpenAI (full penalty controls, logprobs).

Creative writing / brainstorming – Claude or Kimi (finer sampling, long‑thinking mode on Kimi).

Complex AI agents with tool calling – Kimi or DeepSeek (both expose tools and logprobs for debugging).

Very long context (>200 K tokens) – MiniMax or Claude (large windows, stable performance).

Multimodal inputs (image, video, audio) – Kimi k2.5 or Gemini (native multimodal pipelines).

Cost‑sensitive deployments – DeepSeek and Yi (competitive pricing, comparable parameter sets).

Need token‑level probability information – DeepSeek, OpenAI, MiniMax (expose logprobs).

Suppress repetition in long generations – Yi’s repetition_penalty or DeepSeek’s frequency_penalty.

Key Takeaways

Parameter compatibility does not guarantee identical generation behavior; always verify field existence, mutability, and effect.

DeepSeek offers the closest drop‑in replacement for OpenAI with minimal code changes.

Kimi provides strong multimodal support but requires attention to model‑level locked parameters.

Claude excels at long‑context, high‑quality output but follows a different sampling philosophy.

Gemini’s feature set is the richest for multimodal and safety controls, yet its API diverges most from OpenAI.

Invest early in a unified adapter/gateway layer if you anticipate using multiple providers; it reduces technical debt and centralises governance.