| Signal | Voxtral Small 24B 2507 | Delta | Qwen2.5 VL 72B Instruct |
|---|---|---|---|
Capabilities | 50 | -- | |
Pricing | 0 | 0 | |
Context window size | 72 | 0 | |
Recency | 100 | +41 | |
Output Capacity | 20 | -55 | |
| Overall Result | 1 wins | of 5 | 3 wins |
8
days ranked higher
5
days
17
days ranked higher
Mistral AI
Alibaba
Voxtral Small 24B 2507 saves you $95.00/month
That's $1140.00/year compared to Qwen2.5 VL 72B Instruct at your current usage level of 100K calls/month.
| Metric | Voxtral Small 24B 2507 | Qwen2.5 VL 72B Instruct | Winner |
|---|---|---|---|
| Overall Score | 60 | 60 | -- |
| Rank | #203 | #205 | Voxtral Small 24B 2507 |
| Quality Rank | #203 | #205 | Voxtral Small 24B 2507 |
| Adoption Rank | #203 | #205 | Voxtral Small 24B 2507 |
| Parameters | 24B | 72B | -- |
| Context Window | 32K | 33K | Qwen2.5 VL 72B Instruct |
| Pricing | $0.10/$0.30/M | $0.80/$0.80/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | Voxtral Small 24B 2507 |
| Pricing | 0 | 1 | Qwen2.5 VL 72B Instruct |
| Context window size | 72 | 72 | Qwen2.5 VL 72B Instruct |
| Recency | 100 | 59 | Voxtral Small 24B 2507 |
| Output Capacity | 20 | 75 | Qwen2.5 VL 72B Instruct |
Our composite score (0–100) combines six weighted signals: benchmark performance (25%), pricing efficiency (25%), context window size (15%), model recency (15%), output capacity (10%), and capability versatility (10%). Here's what the scores mean for these two models:
Scores 60/100 (rank #203), placing it in the top 30% of all 290 models tracked.
Scores 60/100 (rank #205), placing it in the top 30% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal — your choice should depend on pricing, latency requirements, and specific feature needs.
Voxtral Small 24B 2507 offers 75% better value per quality point. At 1M tokens/day, you'd spend $6.00/month with Voxtral Small 24B 2507 vs $24.00/month with Qwen2.5 VL 72B Instruct — a $18.00 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. Voxtral Small 24B 2507 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (33K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.30/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (60/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input — can analyze screenshots, diagrams, photos, and scanned documents directly
Voxtral Small 24B 2507 and Qwen2.5 VL 72B Instruct are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Voxtral Small 24B 2507
Marginally better benchmark scores; both are excellent
Best for Cost
Voxtral Small 24B 2507
75% lower pricing; better value at scale
Best for Reliability
Voxtral Small 24B 2507
Higher uptime and faster response speeds
Best for Prototyping
Voxtral Small 24B 2507
Stronger community support and better developer experience
Best for Production
Voxtral Small 24B 2507
Wider enterprise adoption and proven at scale
by Mistral AI
| Capability | Voxtral Small 24B 2507 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Mistral AI
Alibaba
Voxtral Small 24B 2507 saves you $1.86/month
That's 77% cheaper than Qwen2.5 VL 72B Instruct at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Voxtral Small 24B 2507 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Context Window | 32K | 33K |
| Max Output Tokens | -- | 32,768 |
| Open Source | Yes | Yes |
| Created | Oct 30, 2025 | Feb 1, 2025 |
Both Voxtral Small 24B 2507 and Qwen2.5 VL 72B Instruct score 60/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
Voxtral Small 24B 2507 is ranked #203 and Qwen2.5 VL 72B Instruct is ranked #205 out of 290+ AI models. Rankings use a composite score combining benchmark performance (25%), pricing (25%), context window (15%), recency (15%), output capacity (10%), and versatility (10%). Scores update hourly.
Voxtral Small 24B 2507 is cheaper at $0.30/M output tokens vs Qwen2.5 VL 72B Instruct's $0.80/M output tokens — 2.7x more expensive. Input token pricing: Voxtral Small 24B 2507 at $0.10/M vs Qwen2.5 VL 72B Instruct at $0.80/M.
Qwen2.5 VL 72B Instruct has a larger context window of 32,768 tokens compared to Voxtral Small 24B 2507's 32,000 tokens. A larger context window means the model can process longer documents and conversations.