GPT-4o
OpenAI • text+image+file->text
Context Window128K
Knowledge CutoffMay 13, 2024
GPT-4o vs Qwen2.5 Coder 32B Instruct
GPT-4o for broad production coding tasks, Qwen2.5 Coder 32B for self-hosted code-focused inference.
Open model page
Qwen2.5 Coder 32B Instruct
Qwen • text->text
Context Window33K
Knowledge CutoffNov 11, 2024
Editorial Verdict
GPT-4o is the general-purpose coding leader; Qwen2.5 Coder 32B is the self-hosted coding option.
GPT-4o significantly outperforms Qwen2.5 Coder 32B on general coding benchmarks while Qwen2.5 Coder 32B is purpose-built for coding and excels within its size class. GPT-4o is the better general coding model. Qwen2.5 Coder 32B is a strong self-hosted option for teams that can't afford or don't need GPT-4o-tier capability.
GPT-4o
- Higher coding benchmark scores across diverse programming languages and tasks
- Mature API with reliable function calling and structured output
- Multimodal capabilities alongside coding strength
Best for: Teams wanting the best general coding model with mature API
Qwen2.5 Coder 32B Instruct
- Purpose-built coding model optimized for code generation tasks
- Self-hostable with open weights and full data privacy
- Strong coding performance in a smaller model suitable for efficient self-hosting
Best for: Self-hosted coding deployments with budget or data privacy constraints
Pricing sourced from OpenRouter — updates as their catalog changes.
Pricing Comparison
All values pull from OpenRouter and update as their catalog changes.
| Metric | GPT-4o | Qwen2.5 Coder 32B Instruct | Winner |
|---|---|---|---|
| Input (per 1M tokens) | $2.50 | $0.66 | 🏆 Qwen2.5 Coder 32B Instruct |
| Output (per 1M tokens) | $10.00 | $1.00 | 🏆 Qwen2.5 Coder 32B Instruct |
| Request fee | N/A | N/A | N/A |
| Image fee | N/A | N/A | N/A |
Cost Estimator
Estimate monthly billing using real OpenRouter prices.
GPT-4o Total
$237.5Qwen2.5 Coder 32B Instruct Total
$38.1Estimated Monthly Savings: $199.4 with Qwen2.5 Coder 32B Instruct
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
GPT-4oMMLU Signal (Reasoning)Qwen2.5 Coder 32B Instruct
69%
71%
GPT-4oHumanEval Signal (Coding)Qwen2.5 Coder 32B Instruct
76%
81%
GPT-4oAgentic Tooling SignalQwen2.5 Coder 32B Instruct
84%
79%
GPT-4oMultimodal SignalQwen2.5 Coder 32B Instruct
78%
64%
Capabilities Matrix
Feature highlights for architecture and production fit.
GPT-4o Strengths
- Supports image inputs for multimodal analysis workflows.
- Strong tool-calling and structured response support.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
Qwen2.5 Coder 32B Instruct Strengths
- Strong tool-calling and structured response support.
- Balanced general-purpose profile for chat, extraction, and automation tasks.
API Implementation
Quick start snippets for each provider style.
OpenAI (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Qwen (Python)
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="qwen/qwen-2.5-coder-32b-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Choose GPT-4o when...
- Your application includes visual reasoning and image understanding tasks.
- You rely on function calling, tool chaining, and structured output contracts.
- You want a balanced default for mixed chat and workflow automation workloads.
Choose Qwen2.5 Coder 32B Instruct when...
- You rely on function calling, tool chaining, and structured output contracts.
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Frequently Asked Questions
Practical checks before selecting a production model.
Is GPT-4o better than Qwen2.5 Coder 32B?
It depends on the workload. GPT-4o and Qwen2.5 Coder 32B differ in pricing, context window, and capability mix — see the side-by-side table above for the breakdown.
What's the price difference between GPT-4o and Qwen2.5 Coder 32B?
GPT-4o: $2.50 per 1M input tokens. Qwen2.5 Coder 32B: $0.66 per 1M input tokens. Output token pricing typically runs 2-5x higher.
Which model is better for coding?
Coding preference depends on your stack and tool-calling needs. Compare the coding signal row, test with your repository tasks, and validate latency in your target region.
Which model is cheaper at scale?
Input and output token pricing can diverge by workload profile. Use the estimator with your monthly request count and token mix to get a realistic cost difference.
Are these official benchmark numbers?
Pricing is live from OpenRouter. Benchmark rows are ModelsAtlas metadata-based signals and should be treated as directional guidance, not official leaderboard scores.
Related Comparisons
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