o3 vs Qwen3.5-9B
Complete pricing and performance comparison between OpenAI's o3 and Alibaba's Qwen3.5-9B.
Quick Verdict
Cheaper
Qwen3.5-9B
40.0x cheaper input, 53.3x cheaper output
Larger Context
Qwen3.5-9B
262K vs 200K
Pricing Comparison
| Spec | o3 | Qwen3.5-9B | Difference |
|---|---|---|---|
| Provider | OpenAI | Alibaba | |
| Input / 1M tokens | $2 | $0.05 | Qwen3.5-9B is 98% more expensive |
| Output / 1M tokens | $8 | $0.15 | Qwen3.5-9B is 98% more expensive |
| Context Window | 200K | 262K | 1x difference |
| Max Output | 100K | 33K | |
| Tokenizer | o200k_base | cl100k_base |
Performance Benchmarks
| Metric | o3 | Qwen3.5-9B | Winner |
|---|---|---|---|
| Quality Index | 38 | -- | N/A |
| Output Speed | 77 tok/s | -- | N/A |
| Value (Quality/$) | 19.2 | -- | Higher = better value |
Benchmark data from Artificial Analysis. Quality Index is a composite score across reasoning, coding, and knowledge tasks.
Cost at Scale
Estimated cost at different usage levels (3:1 input-to-output token ratio, typical for chat).
| Usage | o3 | Qwen3.5-9B | Savings |
|---|---|---|---|
Single request 1K in / 300 out | $0.0044 | $0.0001 | Qwen3.5-9B saves $0.0043 |
10 requests 10K in / 3K out | $0.044 | $0.0009 | Qwen3.5-9B saves $0.043 |
100 requests 100K in / 30K out | $0.440 | $0.0095 | Qwen3.5-9B saves $0.430 |
1,000 requests 1M in / 300K out | $4.40 | $0.095 | Qwen3.5-9B saves $4.31 |
10,000 requests 10M in / 3M out | $44.00 | $0.950 | Qwen3.5-9B saves $43.05 |
1M requests/mo 1B in / 300M out | $4400.00 | $95.00 | Qwen3.5-9B saves $4305.00 |
Pros & Cons
o3 Strengths
- +Higher max output tokens
Qwen3.5-9B Strengths
- +Cheaper input tokens
- +Cheaper output tokens
- +Larger context window (262K vs 200K)
When to Use Each Model
Choose o3 for
- →Generating long-form content or detailed code
Choose Qwen3.5-9B for
- →Budget-conscious projects where cost is the primary factor
- →Long documents, large codebases, or multi-turn conversations
Frequently Asked Questions
Which is cheaper, o3 or Qwen3.5-9B?
For input tokens, Qwen3.5-9B is 40.0x cheaper at $0.05/1M tokens. For output tokens, Qwen3.5-9B is 53.3x cheaper at $0.15/1M tokens. At typical usage (1M input + 300K output), o3 costs $4.40 vs Qwen3.5-9B at $0.095.
What's the context window difference?
o3 supports 200K context (200,000 tokens), while Qwen3.5-9B supports 262K (262,144 tokens). Qwen3.5-9B can handle 1x more context in a single request.
Which model has better benchmarks?
When should I choose o3 over Qwen3.5-9B?
Choose o3 when you need: Higher max output tokens. Choose Qwen3.5-9B when you need: Cheaper input tokens, Cheaper output tokens, Larger context window (262K vs 200K).
How much would 10,000 API requests cost?
At 1K input + 300 output tokens per request (typical chat): o3 = $44.00, Qwen3.5-9B = $0.950. At 10K input + 1K output per request (longer conversations): o3 = $280.00, Qwen3.5-9B = $6.50.