Best Open Source LLMs to Replace Sonnet 4.5 or Opus 4.6: Affordable AI Coding Alternatives 2026

Claude Sonnet 4.5 or Opus 4.6 works well for coding, but it’s expensive. Plenty of developers are looking for alternatives that don’t cost as much. The open source AI space has grown a lot since 2025, and now there are models that can do what Claude does for much less money.

This guide covers five open source models that can replace Claude Sonnet 4.5 or Opus 4.6 for coding: GLM-5, Kimi K2.5, Qwen-Max, MiniMax M2.5, and Devstral 2. These models handle reasoning, code generation, and agent tasks well, and they cost significantly less than Claude.

Why Consider Open Source LLM Alternatives?

Open source models have improved a lot and can compete with proprietary options. Here’s why they’re worth considering:

Key Performance Areas to Consider

When evaluating LLM alternatives, several factors matter:

• Coding Capabilities: How well the model generates, debugs, and explains code
• Reasoning Performance: How well it handles complex problems and logical thinking
• Context Length: How much information the model can process at once
• Agentic Tasks: Tool usage, function calling, and multi-step task execution
• Cost-Performance Ratio: How much value you get per dollar spent

What’s New with Claude Sonnet 4.5?

Before looking at alternatives, it helps to understand what Claude Sonnet 4.5 does well. Released in early 2025, Claude Sonnet 4.5 is Anthropic’s latest model:

Claude Sonnet 4.5 and Opus 4.6 is powerful, but the price makes it hard to justify for many developers and businesses. Open source alternatives offer similar performance for much less money.

1. GLM-5: Agentic engineering with record-low hallucinations

GLM-5 is Z.AI’s new flagship, and the jump from GLM-4.7 is significant. It went from 357B to 744B total parameters while keeping MoE efficiency at 40B active. Two things stand out: DeepSeek Sparse Attention for handling long contexts without blowing up inference costs, and a new RL infrastructure called “slime” that brought hallucination rates down to near zero. In coding benchmarks, it’s approaching Claude Opus 4.5 territory.

Technical Specifications

Feature	GLM-5
Total Parameters	744B (MoE)
Active Parameters	40B
Context Length	200K tokens
Architecture	MoE with Sparse Attention
Input Cost	$0.80/M tokens
Output Cost	$2.56/M tokens
License	MIT
Release Date	February 2026

GLM-5 Coding Plans

Key Strengths

Performance Highlights

Here’s where GLM-5 lands on the benchmarks that matter:

• Coding: 95.8% on SWE-bench Verified. It’s also the first open model to break 50 on the Artificial Analysis Intelligence Index v4.0
• Reasoning/math: 93.6% accuracy overall, 92.7% on AIME 2026, 86.0% on GPQA-Diamond
• Agentic work: ELO 1,412 on GDPval-AA (only Claude Opus 4.6 and GPT-5.2 score higher). #1 on Vending Bench 2
• Reliability: 97% success rate across benchmarks, with the lowest hallucination rate of any open model tested

Try GLM-5

Best Use Cases

GLM-5 works well for:

• Long-running agent tasks: Multi-step planning across complex systems
• Production coding: Full-stack development where you need something close to Claude Opus 4.5
• Enterprise work: MIT license and low hallucination rate matter when mistakes are expensive
• Document generation: Can produce business documents in PDF, Word, and Excel formats
• Tool-heavy workflows: Reasoning plus tool integration and search

2. Kimi K2.5: Multimodal coding with agent swarms

Kimi K2.5 is Moonshot AI’s strongest open-source model. It’s natively multimodal (pretrained on ~15T mixed visual and text tokens), which means it can actually look at images and videos, not just text. The standout feature is its agent swarm: it can spin up to 100 sub-agents working in parallel.

Technical Specifications

Feature	Specification
Total Parameters	1 Trillion
Active Parameters	32 Billion
Context Length	256K tokens
Architecture	Mixture-of-Experts (MoE)
Input Cost	$0.60/M tokens
Output Cost	$3.00/M tokens
Release Date	January 2026
Agent Swarm	Up to 100 sub-agents

Outstanding Features

Benchmark results

Kimi K2.5 scores well across coding and vision tasks:

• Coding: 76.8% on SWE-bench Verified, 73.0% on SWE-bench Multilingual, 85.0% on LiveCodeBench v6
• Vision: 78.5% on MMMU-Pro, 84.2% on MathVision, 88.8% on OmniDocBench 1.5
• Agentic: 78.4% on BrowseComp with agent swarm, 50.2% on HLE-Full with tools
• Context: 256K tokens handles medium-sized repositories in one session
• Video: 86.6% on VideoMMMU, 79.8% on LongVideoBench

Access Kimi K2.5 on OpenRouter

When to use it

• Visual coding: Generate code from UI mockups, screenshots, or video demos
• Parallel agent workflows: Agent swarm cuts runtime by up to 4.5x
• Large codebases: 256K context fits entire repositories
• Frontend work: Responsive interfaces with charts and visual elements
• Document processing: Handles 10,000-word papers or 100-page documents with annotations
• Tight budgets: You get a lot of capability per dollar
• Long coding sessions: Keeps conversation history across extended workflows

3. Qwen-Max: Qwen3’s biggest model

Qwen-Max is the top model in the Qwen3 series. It handles coding, reasoning, and general tasks with a 256K context window and uses an OpenAI-compatible API, so integration is straightforward.

Technical Specifications

Feature	Specification
Model Family	Qwen3
Context Length	256K tokens
Architecture	Advanced Transformer
Input Cost	$1.20/M tokens
Output Cost	$6.00/M tokens
Release Date	September 2025
API Compatibility	OpenAI format

What it offers

Benchmark performance

Qwen-Max holds its own across multiple evaluations:

• General: Strong scores on MMLU, MMMU, and HellaSwag
• Coding: Competitive on coding-specific benchmarks
• Long context: Handles large codebases with 256K tokens
• Production use: Consistent and reliable in real deployments
• Multi-task: Performs well across different task types

Development Ecosystem

Explore Qwen-Max on OpenRouter

When to use it

• Enterprise work: Production-grade AI for business-critical tasks
• Full-stack development: Multiple languages and frameworks
• Repository-wide operations: 256K context for large-scale analysis
• Mixed workloads: Coding, reasoning, and general queries in one model
• Existing OpenAI setups: Drop-in replacement with API key swap

4. MiniMax M2.5: SOTA coding at a fraction of the cost

MiniMax M2.5 is the latest in the M2 series, and the jump from M2.1 is substantial. MiniMax trained it with reinforcement learning across more than 200,000 real-world environments, pushing SWE-bench Verified to 80.2%, which beats Claude Opus 4.6 on multiple scaffolds. It ships in two speed tiers: M2.5 at 50 tokens/second and M2.5-Lightning at 100 tokens/second, both priced low enough to run agents continuously without worrying about cost.

Technical Specifications

Feature	MiniMax M2.5
Architecture	Mixture-of-Experts (MoE)
Context Length	200K tokens
M2.5 Input Cost	$0.15/M tokens (50 TPS)
M2.5 Output Cost	$1.20/M tokens (50 TPS)
Lightning Input Cost	$0.30/M tokens (100 TPS)
Lightning Output Cost	$2.40/M tokens (100 TPS)
Release Date	February 12, 2026

MiniMax Coding Plans (10% Off)

What it does well

Benchmark results

• SWE-bench Verified: 80.2% (Droid: 79.7 vs Opus 4.6’s 78.9; OpenCode: 76.1 vs 75.9)
• Multi-SWE-Bench: 51.3%, particularly good at non-Python languages
• BrowseComp: 76.3% with context management, using ~20% fewer search rounds than M2.1
• VIBE Pro: Matches Claude Opus 4.5 on the upgraded benchmark across Web, Android, iOS, and Windows
• Speed: 22.8 minutes average per SWE-bench task, down from 31.3 minutes on M2.1
• Cost per task: About 10% of what Claude Opus 4.6 charges for the same work

Try MiniMax M2.5 Access MiniMax M2.5 API

Best Use Cases

MiniMax M2.5 works well for:

• Always-on coding agents: Cheap enough to run continuously without budget anxiety
• Multi-Language Projects: Trained on 10+ languages across 200,000+ real-world environments
• Full-stack work: System design, APIs, business logic, databases, and frontend across Web, Android, iOS, and Windows
• Office automation: Financial modeling in Excel, report generation in Word, presentations in PowerPoint
• Interactive Coding Assistants: Lightning variant’s 100 TPS makes IDE integration responsive
• Self-Hosting: Open source weights for on-premise deployment with vLLM or SGLang

5. Devstral 2: Dense architecture for repository-scale work

Devstral 2 takes a different approach. While the other models here use MoE, Devstral 2 is a 123B dense transformer. That means all parameters are active on every inference, which gives it better coherence on whole-repository tasks. It ships with Mistral Vibe, a CLI agent for terminal-based automation.

Technical Specifications

Feature	Devstral 2
Total Parameters	123B (Dense)
Active Parameters	123B
Context Length	256K tokens
Architecture	Dense Transformer
Input Cost	$0.40/M tokens
Output Cost	$2.00/M tokens
Release Date	December 2025

Devstral 2 Vibe CLI

What it does well

Benchmark results

• SWE-bench Verified: 72.2%, at the frontier for open-weight models
• Human evaluation: Preferred over DeepSeek V3.2 in Cline-based coding tasks (42.8% win rate)
• Local model: Devstral Small 2 (24B) hits 68.0% SWE-bench, strong for a model you can run locally
• Cost: Up to 7x cheaper than Claude Sonnet on real-world tasks

Try Devstral 2

Best Use Cases

Devstral 2 works well for:

• Agentic Workflows: Using Mistral Vibe CLI for autonomous terminal-based coding
• Repository Refactoring: Dense architecture provides better coherence for large-scale changes
• Local Development: Devstral Small 2 lets you use powerful coding assistance on local hardware
• Secure Environments: Open weights and local deployment options for strict data privacy

Side-by-side comparison

Here’s how all five stack up:

Performance Comparison Table

Benchmark	GLM-5	Kimi K2.5	Qwen-Max	MiniMax M2.5	Devstral 2	Claude Sonnet 4.5
SWE-bench Verified	95.8%	76.8%	Strong	80.2%	72.2%	77.2%
LiveCodeBench v6	Strong	85.0%	Strong	Strong	Strong	84.5%
MMMU-Pro	—	78.5%	—	—	—	74.0%
Context Window	200K	256K	256K	200K	256K	200K
Agent Swarm	No	Yes	No	No	No	No
Vision Support	No	Yes	No	No	No	No
Cost per 1M Input Tokens	$0.80	$0.60	$1.20	$0.15	$0.40	$3.00
Cost per 1M Output Tokens	$2.56	$3.00	$6.00	$1.20	$2.00	$15.00

Feature Comparison Matrix

Getting started

Step 1: Choose Your Access Method

Each model offers multiple access options:

Step 2: Set Up Your Environment

For OpenRouter access (recommended for beginners):

# Install OpenAI SDK
pip install openai

# Set environment variables
export OPENROUTER_API_KEY="your_api_key_here"
export OPENROUTER_BASE_URL="https://openrouter.ai/api/v1"

Step 3: Basic Implementation Example

import openai

client = openai.OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="your_openrouter_api_key"
)

# Use GLM-5 for agentic tasks
response = client.chat.completions.create(
    model="z-ai/glm-5",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Create a Python web scraper for product prices"}
    ]
)

print(response.choices[0].message.content)

Step 4: Optimize for Your Use Case

Cost breakdown

Here’s what you’d actually pay at 10M tokens/month:

Monthly Cost Comparison (Based on 10M tokens usage)

Model	Input Cost	Output Cost	Total Monthly Cost	Savings vs Claude Sonnet 4.5
Claude Sonnet 4.5	$30.00	$150.00	$180.00	Baseline
GLM-5	$8.00	$25.60	$33.60	81.3% savings
Kimi K2.5	$6.00	$30.00	$36.00	80.0% savings
Qwen-Max	$12.00	$60.00	$72.00	60.0% savings
MiniMax M2.5	$1.50	$12.00	$13.50	92.5% savings
Devstral 2	$4.00	$20.00	$24.00	86.7% savings

What the savings mean in practice

• More experimentation: Lower costs let you test and iterate more freely
• Team-wide access: Run AI assistance for your whole team, not just a few developers
• Broader integration: Use AI in more parts of your application
• Faster shipping: More AI-assisted development cycles without budget anxiety

Tips and common mistakes

What works

What to avoid

• Over-Engineering: Don’t use the most expensive model for simple tasks
• Inadequate Testing: Always validate model outputs in your specific domain
• Context Overflow: Monitor token usage to avoid unexpected costs
• Single Model Dependency: Consider using different models for different tasks

What’s coming next for open source LLMs

A few trends worth watching:

• Domain-specific models: More specialized options like Qwen3 Coder
• Better efficiency: More performance per parameter and per dollar
• Tighter tool integration: Better compatibility with IDEs and coding workflows
• Multimodal by default: Vision and audio becoming standard, not optional
• Faster inference: Latency dropping enough for real-time use

Which one should you pick?

It depends on what matters most to you:

GLM-5 if you need:

• Top coding scores: 95.8% SWE-bench Verified, highest among open source
• Agentic engineering: Long-horizon multi-step planning
• Low hallucinations: Record-low rate, good for enterprise
• Strong reasoning: 92.7% on AIME 2026, 86.0% on GPQA-Diamond
• MIT license: Full commercial and self-hosting freedom

Try GLM-5

Kimi K2.5 if you want:

• Vision support: The only model here that can read images and video
• Agent swarms: Parallel execution with up to 100 sub-agents
• 256K context: Fits entire repositories
• Claude Code compatibility: Works with Claude Code and Kimi Code
• Good price: $0.60/M input tokens for all of the above

Try Kimi K2.5

Qwen-Max if you care about:

• All-around capability: Qwen3’s top model, solid across the board
• Production reliability: Stable for business-critical workloads
• OpenAI compatibility: Drop-in replacement for existing setups
• 256K context: Large-scale codebase operations

Explore Qwen-Max

MiniMax M2.5 if you want:

• Frontier coding scores: 80.2% SWE-bench Verified, beating Claude Opus 4.6
• Always-on agents: $1/hour at 100 TPS, $0.30/hour at 50 TPS
• End-to-end coding: System design through code review and testing, not just bug fixes
• Multi-language coding: 10+ languages across 200,000+ real-world environments
• Office automation: Word, PowerPoint, Excel with built-in Office Skills
• Fast inference: Lightning variant runs at 100 tokens/second

MiniMax Coding Plans (10% Off)

Devstral 2 if you need:

• Local deployment: 24B small model runs on consumer GPUs
• Dense reasoning: All parameters active for coherent whole-repo work
• Terminal agent: Native Mistral Vibe CLI integration
• Data privacy: Open weights, run everything locally

Try Devstral 2

Any of these five models will save you money compared to Claude Sonnet 4.5. GLM-5 leads on coding benchmarks, Kimi K2.5 is the only one with vision, MiniMax M2.5 is the cheapest with frontier-level scores, Qwen-Max is the most versatile, and Devstral 2 is the best for local/private use. Pick the one that fits your workflow and budget.