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WizardLM-2 8x22BvsGPT-o3

Microsoft vs OpenAI — Side-by-side model comparison

GPT-o3 leads 4/5 categories

Head-to-Head Comparison

MetricWizardLM-2 8x22BGPT-o3
Provider
Microsoft
Arena Rank
#2
Context Window
64K
200K
Input Pricing
Free (open)/1M tokens
$2.00/1M tokens
Output Pricing
Free (open)/1M tokens
$8.00/1M tokens
Parameters
176B (39B active)
Undisclosed
Open Source
Yes
No
Best For
Complex instructions, reasoning, coding
Advanced reasoning, agentic tasks, research
Release Date
Apr 15, 2024
Apr 16, 2025

WizardLM-2 8x22B

WizardLM-2 8x22B, developed by Microsoft, is an instruction-tuned Mixture-of-Experts model with 176 billion total parameters (39 billion active per token) and a 64K token context window. Built upon the Mixtral 8x22B architecture, it applies Microsoft's WizardLM training methodology to enhance complex instruction following, reasoning, and coding capabilities. The model demonstrates substantial improvements over its base on multi-step reasoning, structured output generation, and nuanced writing tasks. WizardLM-2 uses Evol-Instruct, a method that progressively evolves training instructions to increase complexity and diversity. Free and open-source, it can be deployed on enterprise multi-GPU setups. The model represents Microsoft's contribution to the open-source community through instruction-tuning research that advances the capability of existing base models without requiring new pre-training runs.

GPT-o3

GPT-o3 is OpenAI's most advanced reasoning model, succeeding o1 as the frontier of deliberative AI. It uses an enhanced chain-of-thought approach where the model spends more compute time 'thinking' before responding, dramatically improving performance on complex STEM, mathematical, and logical reasoning tasks. With a 200K token context window and the ability to use tools during reasoning, o3 represents a significant leap in AI problem-solving capabilities. It achieved state-of-the-art results on the ARC-AGI benchmark, demonstrating near-human performance on novel reasoning challenges. The model is particularly strong at multi-step mathematical proofs, complex code debugging, and scientific analysis where careful step-by-step reasoning is essential. Originally priced at a premium, an 80% price reduction in June 2025 made o3 accessible to a much broader range of developers and applications.

View OpenAI profile →

Key Differences: WizardLM-2 8x22B vs GPT-o3

1

GPT-o3 supports a larger context window (200K), allowing it to process longer documents in a single request.

2

WizardLM-2 8x22B is open-source (free to self-host and fine-tune) while GPT-o3 is proprietary (API-only access).

W

When to use WizardLM-2 8x22B

  • +You need to self-host or fine-tune the model
  • +Your use case involves complex instructions, reasoning, coding
View full WizardLM-2 8x22B specs →
G

When to use GPT-o3

  • +You need to process long documents (200K context)
  • +You prefer a managed API without infrastructure overhead
  • +Your use case involves advanced reasoning, agentic tasks, research
View full GPT-o3 specs →

The Verdict

GPT-o3 wins our head-to-head comparison with 4 out of 5 category wins. It's the stronger choice for advanced reasoning, agentic tasks, research, though WizardLM-2 8x22B holds an edge in complex instructions, reasoning, coding.

Last compared: April 2026 · Data sourced from public benchmarks and official pricing pages

Frequently Asked Questions

Which is better, WizardLM-2 8x22B or GPT-o3?
In our head-to-head comparison, GPT-o3 leads in 4 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). GPT-o3 excels at advanced reasoning, agentic tasks, research, while WizardLM-2 8x22B is better suited for complex instructions, reasoning, coding. The best choice depends on your specific requirements, budget, and use case.
How does WizardLM-2 8x22B pricing compare to GPT-o3?
WizardLM-2 8x22B charges Free (open) per 1M input tokens and Free (open) per 1M output tokens. GPT-o3 charges $2.00 per 1M input tokens and $8.00 per 1M output tokens. For high-volume production workloads, the pricing difference can significantly impact total cost of ownership.
What is the context window difference between WizardLM-2 8x22B and GPT-o3?
WizardLM-2 8x22B supports a 64K token context window, while GPT-o3 supports 200K tokens. GPT-o3 can process longer documents, codebases, and conversations in a single request. Context window size matters most for tasks involving long documents, large codebases, or extended conversations.
Can I use WizardLM-2 8x22B or GPT-o3 for free?
WizardLM-2 8x22B is a paid API model starting at Free (open) per 1M input tokens. GPT-o3 is a paid API model starting at $2.00 per 1M input tokens. Open-source models can be self-hosted for free but require your own GPU infrastructure.
Which model has better benchmarks, WizardLM-2 8x22B or GPT-o3?
WizardLM-2 8x22B's arena rank is not yet available, while GPT-o3 holds rank #2. Note that benchmarks don't capture every use case — we recommend testing both models on your specific tasks.
Is WizardLM-2 8x22B or GPT-o3 better for coding?
WizardLM-2 8x22B is specifically optimized for coding tasks. GPT-o3's primary strength is advanced reasoning, agentic tasks, research. For coding specifically, arena rank and code-specific benchmarks are the best indicators of performance.