GPT-4o
OpenAI • Multimodal
Context Window128K
Knowledge CutoffUnknown
GPT-4o vs Qwen2.5 Coder 32B
GPT-4o for broad production coding tasks, Qwen2.5 Coder 32B for self-hosted code-focused inference.
Open model page
Qwen2.5 Coder 32B
Qwen • Code
Context Window128K
Knowledge CutoffUnknown
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
- 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 | Winner |
|---|---|---|---|
| Input (per 1M tokens) | Custom | Custom | N/A |
| Output (per 1M tokens) | Custom | Custom | N/A |
| 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
VariableQwen2.5 Coder 32B Total
VariableSavings unavailable (pricing not published for one model).
Capability Signals
Scores are directional estimates from model metadata — not official benchmark results.
GPT-4oMMLU Signal (Reasoning)Qwen2.5 Coder 32B
69%
69%
GPT-4oHumanEval Signal (Coding)Qwen2.5 Coder 32B
69%
79%
GPT-4oAgentic Tooling SignalQwen2.5 Coder 32B
69%
69%
GPT-4oMultimodal SignalQwen2.5 Coder 32B
69%
69%
Capabilities Matrix
Feature highlights for architecture and production fit.
GPT-4o Strengths
- Balanced general-purpose profile for chat, extraction, and automation tasks.
Qwen2.5 Coder 32B Strengths
- 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/qwen2.5-coder-32b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Choose GPT-4o when...
- You want a balanced default for mixed chat and workflow automation workloads.
- You can measure quality with your own benchmark and prompt set.
Choose Qwen2.5 Coder 32B when...
- 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.
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.
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