Purpose

Comprehensive ranking of top open-source language models as of December 2025, with benchmark comparisons, use case recommendations, and specialization analysis.

Top Open Source Models (2025 Rankings)

Elite Tier (Top 5)

RankModelParametersKey StrengthsArena Score
1Qwen3-235B235B (~22B active MoE)Reasoning, coding, multilingual975.53*
2DeepSeek-V3.1671B (~37B active MoE)Cost-efficient, reasoning hybrid modes959.80
3GLM-4.5UnknownTool use (90.6%), web browsing, agentic workflows-
4Kimi K21T (~32B active MoE)Agentic intelligence, 200-300 sequential tool callsLMSYS #1 OSS
5Llama 4 Scout10M contextMultimodal, extreme long context, stable ecosystem-

*Note: Qwen2.5-Max score; Qwen3 ranks higher but separate leaderboard entry

High-Performance Tier (70B+ Models)

ModelSizeBest ForKey Benchmarks
Llama 3.3 70B70BStructured writing, clean formattingTool Use: 77.3 (BFCL)
Qwen2.5 72B72BMath/research reasoning, asking clarifying questionsMMLU-Pro CS: 78%
DeepSeek 67B67BCoding (HumanEval leader)-
Mixtral 8x22B176B (~22B active)General chat/reasoning, efficiency-

Mid-Size Tier (7-32B Models)

ModelSizeBest ForPerformance Notes
Qwen2.5-Coder-32B32BCoding specialist, 300+ languagesArena: 901.98
DeepSeek-R1-Distill-Qwen-32B32BAdvanced reasoningBeats OpenAI-o1-mini
DeepSeek Coder V2VariousCode generation/completion specialistState-of-the-art coding
Gemma 3-27B27BEfficiency, Top 10 Arena ranking-

Detailed Model Analysis

1. Qwen3 Series (Alibaba) - April 2025

Architecture: Hybrid Mixture-of-Experts (MoE)

Model Variants:

  • Qwen3-235B-A22B (flagship: 235B total, ~22B active)
  • Qwen3-30B-A3B
  • Qwen3-8B, 4B, 1.7B, 0.6B (dense models)

Key Advantages:

  • Meets or beats GPT-4o and DeepSeek-V3 on most benchmarks
  • Superior multilingual capabilities
  • Native 32K context (expandable to 131K with YaRN)
  • Apache 2.0 license

Benchmark Performance:

  • ArenaHard: 91.0 (vs 85.5 for DeepSeek-V3, 85.3 for GPT-4o)
  • AIME’24/25: 80.4 (ahead of QwQ-32B)
  • Best open-source LLM as of April 2025

Use Cases: General-purpose excellence, multilingual applications, reasoning tasks

2. DeepSeek V3.1 / DeepSeek-R1 Series

Architecture: Hybrid system with switchable modes

Key Features:

  • Released August 2025 (V3.1)
  • “Thinking” mode for complex reasoning
  • “Non-thinking” mode for faster responses
  • DeepSeek-R1: Specialized for financial analysis, complex math, theorem proving

Benchmark Performance:

  • LMSYS Arena: 959.80 (2nd overall)
  • MMLU-Pro CS: 78%
  • MATH-500: 94.5% (DeepSeek-R1-Distill, best score)
  • Tool Use (BFCL): 58.55

Specializations:

  • DeepSeek-R1: Advanced reasoning
  • DeepSeek 67B: Heavy coding (HumanEval leader)
  • DeepSeek Coder V2: Code specialist (300+ languages)

Cost Efficiency: 7.3x better pricing than proprietary models

3. GLM-4.5 (Zhipu AI)

Status: Called “best open-source AI model” in recent comparisons

Standout Benchmarks:

  • Tool use: 90.6% success rate (beats Claude 4 Sonnet, Kimi-K2, Qwen3)
  • Web browsing (BrowseComp): Beats Claude-4-Opus by 8 points
  • Consistently top-tier across the board

Key Strengths:

  • Superior tool use capabilities
  • Strong web browsing/information retrieval
  • Excellent for agentic workflows

4. Kimi K2 (Moonshot AI) - July 2025

Architecture: 1 trillion total parameters, 32B activated (MoE)

Context: 128K tokens

LMSYS Rankings (July 17, 2025):

  • #1 open-source model
  • #5 overall (based on 3,000+ user votes)

Benchmark Performance:

  • Tau2-bench: 66.1
  • ACEBench (en): 76.5
  • SWE-bench Verified: 65.8
  • SWE-bench Multilingual: 47.3
  • LiveCodeBench v6: 53.7
  • AIME 2025: 49.5
  • GPQA-Diamond: 75.1
  • OJBench: 27.1
  • SimpleQA: 31.0% (leads open-source)
  • MMLU: 89.5%
  • MMLU-Redux: 92.7%
  • IFEval: 89.8% (leads all models)
  • Multi-Challenge: 54.1%

Agentic Capabilities (Kimi K2 Thinking variant):

  • Executes 200-300 sequential tool calls autonomously
  • Released November 2025
  • Beats GPT-5 and Claude Sonnet 4.5 on major benchmarks
  • Ideal for complex agentic workflows
  • Can independently run shell commands, build apps/websites, call APIs, automate data science

vs Llama 4: Kimi K2 specifically post-trained for agentic workflows; stronger in autonomous behavior than Llama 4

5. Llama Series (Meta)

Context: 128K tokens (3.1+)

Strengths:

  • Stable, huge ecosystem
  • Llama 4 Scout: 10M token context, strong multimodal
  • Llama 3.3 70B: Cleanest formatting, best for structured docs/ops
  • Llama 3.1 405B: Largest open model (requires distributed deployment)

Benchmark Performance:

  • Llama 3.1 405B Tool Use (BFCL): 81.1
  • Llama 3.3 70B Tool Use: 77.3
  • Llama 3.3 70B: Solid general-purpose performance

Ecosystem: Most mature, fewer dramatic changes, “dependable plateau”

6. Mixtral 8x22B (Mistral AI)

Architecture: 176B total, ~22B active (MoE)

Use Cases:

  • General chat/reasoning
  • Speed/cost efficiency balance
  • Alternative to larger dense models

7. Gemma 3 (Google)

Variants: 1B, 4B, 27B

Strengths:

  • Top 10 Arena ranking (27B)
  • Efficiency-focused
  • Good for resource-constrained deployments

Benchmark Comparison Matrix

Reasoning & Math

ModelAIME 2025MATH-500GPQA-Diamond
Qwen380.4--
DeepSeek-R1-Distill-94.5%-
Kimi K249.5-75.1

Coding Performance

ModelSWE-bench VerifiedLiveCodeBenchHumanEval
Kimi K265.853.7-
DeepSeek 67B--Leader
Qwen2.5-Coder-32B--Strong

Tool Use & Agentic Capabilities

ModelTool Use ScoreSequential Tool CallsAgentic Workflow
GLM-4.590.6%-Excellent
Kimi K2 Thinking-200-300Best-in-class
Llama 3.1 405B81.1 (BFCL)-Good
Llama 3.3 70B77.3 (BFCL)-Good

Instruction Following & Knowledge

ModelIFEvalMMLUSimpleQA
Kimi K289.8%89.5%31.0%
Qwen3---

Use Case Recommendations

By Task Type

TaskBest ModelAlternativeRationale
Structured writing/docsLlama 3.3 70BQwen2.5 72BCleanest formatting, minimal manual intervention
Math/research reasoningQwen 2.5 72BDeepSeek-R1Asks clarifying questions, doesn’t fabricate
Heavy codingDeepSeek 67BQwen2.5-Coder-32BHumanEval leader, 300+ languages
Agentic workflowsKimi K2 ThinkingGLM-4.5200-300 tool calls, autonomous execution
Tool useGLM-4.5Kimi K290.6% success rate
MultilingualQwen3-Distinct advantage
Long context (10M+)Llama 4-10M token context
Long context (128K)Qwen3 / Llama 3.1-Native 128K support
Speed/cost efficiencyMixtral / Gemma 38-14B classBalanced performance
General purpose excellenceQwen3-235BDeepSeek-V3.1Beats GPT-4o on most benchmarks

By Use Case Priority

Production Systems (Quality Critical):

  1. Qwen3-235B (general)
  2. GLM-4.5 (tool use)
  3. DeepSeek-R1 (reasoning)

Development/Testing (Balanced):

  1. Llama 3.3 70B (structured output)
  2. Qwen2.5-Coder-32B (coding)
  3. Mixtral 8x22B (general)

Autonomous Agents (Agentic Intelligence):

  1. Kimi K2 Thinking (200-300 sequential tool calls)
  2. GLM-4.5 (90.6% tool use success)
  3. Qwen3 (strong reasoning)

Cost-Sensitive Operations:

  1. DeepSeek V3.1 (7.3x better pricing)
  2. Gemma 3 (efficiency)
  3. Mixtral 8x22B (MoE efficiency)

Model Specializations Summary

Coding Excellence

  • DeepSeek 67B / Coder V2: HumanEval leader, 300+ languages
  • Qwen2.5-Coder-32B: Arena 901.98, May 2025 coding leader
  • Kimi K2: Strong LiveCodeBench (53.7), SWE-bench Verified (65.8)

Reasoning & Math

  • DeepSeek-R1-Distill: 94.5% MATH-500 (best)
  • Qwen3: 91.0 ArenaHard, 80.4 AIME
  • Kimi K2: 75.1 GPQA-Diamond

Multilingual

  • Qwen3: Distinct multilingual advantage
  • Kimi K2: 47.3 SWE-bench Multilingual

Long Context

  • Llama 4: 10M tokens
  • Qwen3: 32K native (expandable to 131K)
  • Llama 3.1: 128K
  • Kimi K2: 128K

Agentic Workflows

  • Kimi K2 Thinking: 200-300 sequential tool calls (best)
  • GLM-4.5: 90.6% tool use success
  • DeepSeek-V3.1: Hybrid thinking/non-thinking modes

Value Analysis (2025)

Cost-Performance Leaders:

  • Qwen3-235B: Quality 50-57, $0.17-0.42/M tokens
  • DeepSeek V3.2: Quality 50-57, $0.17-0.42/M tokens
  • Llama 3.3 70B: Quality 50-57, $0.17-0.42/M tokens

Performance Gap: Open source models now 7.3x better pricing than proprietary, with performance gap closing rapidly.

Licensing

Most models listed use Apache 2.0 or similar permissive licenses, enabling commercial use. Always verify specific license terms for production deployments.

Sources

  1. 10 Best Open-Source LLM Models (2025 Updated): Llama 4, Qwen 3 and DeepSeek R1 - Hugging Face
  2. Top 10 Open LLMs 2025 November Ranking & Analysis - Skywork AI
  3. The 11 best open-source LLMs for 2025 - n8n Blog
  4. Top 9 Large Language Models as of December 2025 - Shakudo
  5. Open Source vs Proprietary LLMs: Complete 2025 Benchmark Analysis - What LLM?
  6. LLM Leaderboard - Artificial Analysis
  7. Kimi K2: Open Agentic Intelligence - Moonshot AI
  8. Kimi K2: Open Agentic Intelligence - arXiv
  9. GLM 4.5: The best Open-Source AI model, beats Kimi-K2, Qwen3 - Medium
  10. Kimi-k2 Benchmarks explained - Medium
  11. Kimi K2 Thinking: Open-Source LLM Guide, Benchmarks, and Tools - DataCamp
  12. Kimi K2 vs Llama 4: Which is the Best Open Source Model? - Analytics Vidhya