Qwen 2.5 VL 7B vs Codestral
Compare Qwen 2.5 VL 7B and Codestral: pricing, performance, context window, latency, and best use cases. Side-by-side comparison on XALEN.
Updated 2026-05-21 · By Abhishek Raj · Our methodology
| Feature | Qwen 2.5 VL 7B | Codestral |
|---|---|---|
| Category | Vision | Code |
| Parameters | 7B | 22B |
| Context Window | 128K | 256K |
| Input Price | $0.01/1M tokens | $0.03/1M tokens |
| Output Price | $0.02/1M tokens | $0.05/1M tokens |
| Latency | ~150ms | ~200ms |
Choose Qwen 2.5 VL 7B when:
- ✓ Budget image analysis
- ✓ Simple OCR
- ✓ Quick visual Q&A
Low cost vision, Asian language OCR, Fast
Choose Codestral when:
- ✓ API integration code
- ✓ SDK generation
- ✓ Code review
256K context for code, Strong code generation, Good APIs
Verdict: Qwen 2.5 VL 7B vs Codestral
For cost efficiency, Qwen 2.5 VL 7B wins at $0.01/1M input tokens. For speed, Qwen 2.5 VL 7B is faster at ~150ms. Qwen 2.5 VL 7B excels at Budget image analysis while Codestral is better for API integration code. Both are available on XALEN through a single API — try them in the Playground to see which fits your workload.
Detailed Analysis
Pricing Comparison
Qwen 2.5 VL 7B costs $0.01/1M input tokens and $0.02/1M output tokens. Codestral costs $0.03 input and $0.05 output. Qwen 2.5 VL 7B is 3.0x cheaper on input tokens. XALEN offers batch processing at 50% discount on both models.
Performance & Context
Qwen 2.5 VL 7B has a 128K context window with ~150ms latency. Codestral offers 256K context at ~200ms. Codestral has the larger context window.
Best For
Qwen 2.5 VL 7B (Vision) is optimized for: Budget image analysis, Simple OCR, Quick visual Q&A. Codestral (Code) works best for: API integration code, SDK generation, Code review.
Try Both on XALEN
Both models are available through XALEN's OpenAI-compatible API. Switch between them by changing the model parameter:
from xalen import XALEN
client = XALEN(api_key="xln_test_YOUR_KEY")
# Use Qwen 2.5 VL 7B
response_a = client.chat.completions.create(
model="qwen-2-5-vl-7b",
messages=[{"role": "user", "content": "Your question here"}]
)
# Use Codestral
response_b = client.chat.completions.create(
model="codestral",
messages=[{"role": "user", "content": "Your question here"}]
)
Frequently Asked Questions
Which is better, Qwen 2.5 VL 7B or Codestral?
Qwen 2.5 VL 7B (Vision, 7B) offers Low cost vision. Codestral (Code, 22B) offers 256K context for code. Choose Qwen 2.5 VL 7B for Budget image analysis or Codestral for API integration code.
How much does Qwen 2.5 VL 7B cost vs Codestral?
Qwen 2.5 VL 7B: $0.01/1M input, $0.02/1M output. Codestral: $0.03/1M input, $0.05/1M output. Both available on XALEN with batch processing at 50% discount.
Can I use both models on XALEN?
Yes. XALEN provides 200+ models through a single OpenAI-compatible API. Switch between Qwen 2.5 VL 7B and Codestral by changing the model parameter. No code changes needed.
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Last updated: 2026-05-21. Pricing and specifications may change. Check pricing page for latest rates.