GPT-3.5-turbo vs Sonar vs Qwen Plus Character (Japanese)
Qwen Plus Character (Japanese) comes out ahead, 36 to 30 and 28 on our weighted score, and it is the cheaper option too.
OpenAIGPT-3.5-turbo
28/100- ECI—
- Price$0.50 / $1.50
- Context16K
PerplexitySonar
30/100- ECI—
- Price$1.00 / $1.00
- Context128K
- Our pick
Alibaba (Qwen)Qwen Plus Character (Japanese)
36/100- ECI—
- Price$0.50 / $1.40
- Context8K
Qwen Plus Character (Japanese) is our pick
Qwen Plus Character (Japanese) is the better all-round choice, scoring 36/100 against Sonar (30) and GPT-3.5-turbo (28). It leads on inputs & features. Sonar wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen Plus Character (Japanese)Qwen Plus Character (Japanese) $0.725 · GPT-3.5-turbo $0.75 · Sonar $1.00 per 1M tokens (3:1 blend)
- Longest contextSonarSonar 128,000 · GPT-3.5-turbo 16,385 · Qwen Plus Character (Japanese) 8,192 tokens
- Widest inputsSame inputsGPT-3.5-turbo: Text · Sonar: Text · Qwen Plus Character (Japanese): Text
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-3.5-turbo | Sonar | Qwen Plus Character (Japanese) |
|---|---|---|---|---|
| Price | 50% | 56 | 50 | 57 |
| Inputs & features | 30% | 0 | 0 | 25 |
| Context window | 20% | 0 | 24 | 0 |
| Overall | 100% | 28/100 | 30/100 | 36/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT-3.5-turboOpenAI | SonarPerplexity | Qwen Plus Character (Japanese)Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.50 (best) | $1.00 | $0.50 (best) |
| Output | $1.50 | $1.00 (best) | $1.40 |
| Cached input | Free | — | — |
| Blended (3:1) | $0.75 | $1.00 | $0.725 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Perplexity API | Official Alibaba API |
| Limits | |||
| Context window | 16,385 tokens | 128,000 tokens (best) | 8,192 tokens |
| Max output | 4,096 tokens (best) | 4,096 tokens (best) | 512 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | No | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gpt-3.5-turbo | sonar | qwen-plus-character-ja |
| API providers | 11 (best) | 6 | 1 |
| Released | Mar 1, 2023 | Jan 1, 2024 | Jan 2024 |
| Knowledge cutoff | Sep 1, 2021 | Sep 1, 2025 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-3.5-turbo$8.00
- Sonar$12.00
- Qwen Plus Character (Japanese)$7.80
Which should you choose?
Which is better: GPT-3.5-turbo, Sonar or Qwen Plus Character (Japanese)?
Qwen Plus Character (Japanese) is the better all-round choice, scoring 36/100 against Sonar (30) and GPT-3.5-turbo (28). It leads on inputs & features. Sonar wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GPT-3.5-turbo, Sonar or Qwen Plus Character (Japanese)?
Qwen Plus Character (Japanese) is cheaper at $0.50 input / $1.40 output per million tokens (official Alibaba API price). GPT-3.5-turbo costs $0.50 input / $1.50 output per million tokens (official OpenAI API price); Sonar costs $1.00 input / $1.00 output per million tokens (official Perplexity API price). At a typical mix of three input tokens to one output token, that is $0.725 per million tokens for Qwen Plus Character (Japanese) versus $0.75 for GPT-3.5-turbo (1× as much) and $1.00 for Sonar (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-3.5-turbo has not been scored yet, Sonar has not been scored yet and Qwen Plus Character (Japanese) has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-3.5-turbo, Sonar and Qwen Plus Character (Japanese) yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that GPT-3.5-turbo and Sonar does not support tool calling, which most coding agents need.
Which has the bigger context window?
Sonar has the largest context window at 128,000 tokens, against 16,385 for GPT-3.5-turbo and 8,192 for Qwen Plus Character (Japanese). Maximum output per response: GPT-3.5-turbo up to 4,096, Sonar up to 4,096, Qwen Plus Character (Japanese) up to 512 tokens.
Which can read images, PDFs, audio or video?
GPT-3.5-turbo accepts text; Sonar accepts text; Qwen Plus Character (Japanese) accepts text. They handle the same number of input types.
Are any of these open source?
No. GPT-3.5-turbo, Sonar and Qwen Plus Character (Japanese) are proprietary and only available through APIs and apps.
Which is newer?
Sonar is the newest, released Jan 1, 2024. Qwen Plus Character (Japanese) came out Jan 2024; GPT-3.5-turbo came out Mar 1, 2023. Knowledge cutoff: GPT-3.5-turbo Sep 1, 2021, Sonar Sep 1, 2025, Qwen Plus Character (Japanese) Apr 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
