| Signal | o1 | Delta | Qwen3 30B A3B Instruct 2507 |
|---|---|---|---|
Capabilities | 83 | +33 | |
Benchmarks | 82 | +82 | |
Pricing | 60 | +60 | |
Context window size | 84 | -2 | |
Recency | 48 | -41 | |
Output Capacity | 83 | -7 | |
| Overall Result | 3 wins | of 6 | 3 wins |
10
days higher
5
days
15
days higher
OpenAI
Alibaba
Qwen3 30B A3B Instruct 2507 saves you $4476.00/month
That's $53712.00/year compared to o1 at your current usage level of 100K calls/month.
| Metric | o1 | Qwen3 30B A3B Instruct 2507 | Winner |
|---|---|---|---|
| Overall Score | 75 | 75 | o1 |
| Rank | #113 | #115 | o1 |
| Quality Rank | #113 | #115 | o1 |
| Adoption Rank | #113 | #115 | o1 |
| Parameters | -- | 30B | -- |
| Context Window | 200K | 262K | Qwen3 30B A3B Instruct 2507 |
| Pricing | $15.00/$60.00/M | $0.09/$0.30/M | -- |
| Signal Scores | |||
| Capabilities | 83 | 50 | o1 |
| Benchmarks | 82 | -- | o1 |
| Pricing | 60 | 0 | o1 |
| Context window size | 84 | 86 | Qwen3 30B A3B Instruct 2507 |
| Recency | 48 | 89 | Qwen3 30B A3B Instruct 2507 |
| Output Capacity | 83 | 90 | Qwen3 30B A3B Instruct 2507 |
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 75/100 (rank #113), placing it in the top 61% of all 290 models tracked.
Scores 75/100 (rank #115), placing it in the top 61% of all 290 models tracked.
With only a 1-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.
Qwen3 30B A3B Instruct 2507 offers 99% better value per quality point. At 1M tokens/day, you'd spend $5.85/month with Qwen3 30B A3B Instruct 2507 vs $1125.00/month with o1 - a $1119.15 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. Qwen3 30B A3B Instruct 2507 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K 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 (75/100) 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
o1 and Qwen3 30B A3B Instruct 2507 are extremely close in overall performance (only 0.6000000000000085 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
o1
Marginally better benchmark scores; both are excellent
Best for Cost
Qwen3 30B A3B Instruct 2507
99% lower pricing; better value at scale
Best for Reliability
o1
Higher uptime and faster response speeds
Best for Prototyping
o1
Stronger community support and better developer experience
Best for Production
o1
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | o1 | Qwen3 30B A3B Instruct 2507 |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
OpenAI
Alibaba
Qwen3 30B A3B Instruct 2507 saves you $98.48/month
That's 99% cheaper than o1 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 | o1 | Qwen3 30B A3B Instruct 2507 |
|---|---|---|
| Context Window | 200K | 262K |
| Max Output Tokens | 100,000 | 262,144 |
| Open Source | No | Yes |
| Created | Dec 17, 2024 | Jul 29, 2025 |
o1 scores 75/100 (rank #113) compared to Qwen3 30B A3B Instruct 2507's 75/100 (rank #115), giving it a 1-point advantage. o1 is the stronger overall choice, though Qwen3 30B A3B Instruct 2507 may excel in specific areas like cost efficiency.
o1 is ranked #113 and Qwen3 30B A3B Instruct 2507 is ranked #115 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.
Qwen3 30B A3B Instruct 2507 is cheaper at $0.30/M output tokens vs o1's $60.00/M output tokens - 200.0x more expensive. Input token pricing: o1 at $15.00/M vs Qwen3 30B A3B Instruct 2507 at $0.09/M.
Qwen3 30B A3B Instruct 2507 has a larger context window of 262,144 tokens compared to o1's 200,000 tokens. A larger context window means the model can process longer documents and conversations.