GPT-4 Turbo vs Qwen Plus Character (Japanese) vs Sonar Pro
Qwen Plus Character (Japanese) comes out ahead, 36 to 20 and 20 on our weighted score, and it is the cheaper option too.
OpenAIGPT-4 Turbo
20/100- ECI—
- Price$10.00 / $30.00
- Context128K
- Our pick
Alibaba (Qwen)Qwen Plus Character (Japanese)
36/100- ECI—
- Price$0.50 / $1.40
- Context8K
PerplexitySonar Pro
20/100- ECI—
- Price$3.00 / $15.00
- Context200K
Qwen Plus Character (Japanese) is our pick
Qwen Plus Character (Japanese) is the better all-round choice, scoring 36/100 against Sonar Pro (20) and GPT-4 Turbo (20). It leads on price. GPT-4 Turbo wins on inputs & features. Sonar Pro 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 · Sonar Pro $6.00 · GPT-4 Turbo $15.00 per 1M tokens (3:1 blend)
- Longest contextSonar ProSonar Pro 200,000 · GPT-4 Turbo 128,000 · Qwen Plus Character (Japanese) 8,192 tokens
- Widest inputsGPT-4 Turbo and Sonar ProGPT-4 Turbo: Text, Images · Qwen Plus Character (Japanese): Text · Sonar Pro: Text, Images
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GPT-4 Turbo | Qwen Plus Character (Japanese) | Sonar Pro |
|---|---|---|---|---|
| Price | 50% | 0 | 57 | 13 |
| Inputs & features | 30% | 50 | 25 | 25 |
| Context window | 20% | 24 | 0 | 32 |
| Overall | 100% | 20/100 | 36/100 | 20/100 |
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-4 TurboOpenAI | Qwen Plus Character (Japanese)Alibaba (Qwen) | Sonar ProPerplexity |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $10.00 | $0.50 (best) | $3.00 |
| Output | $30.00 | $1.40 (best) | $15.00 |
| Cached input | — | — | — |
| Blended (3:1) | $15.00 | $0.725 (best) | $6.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Alibaba API | Official Perplexity API |
| Limits | |||
| Context window | 128,000 tokens | 8,192 tokens | 200,000 tokens (best) |
| Max output | 4,096 tokens | 512 tokens | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | gpt-4-turbo | qwen-plus-character-ja | sonar-pro |
| API providers | 12 (best) | 1 | 5 |
| Released | Nov 6, 2023 | Jan 2024 | Jan 1, 2024 |
| Knowledge cutoff | Dec 2023 | Apr 2024 | Sep 1, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-4 Turbo$160.00
- Qwen Plus Character (Japanese)$7.80
- Sonar Pro$60.00
Which should you choose?
Which is better: GPT-4 Turbo, Qwen Plus Character (Japanese) or Sonar Pro?
Qwen Plus Character (Japanese) is the better all-round choice, scoring 36/100 against Sonar Pro (20) and GPT-4 Turbo (20). It leads on price. GPT-4 Turbo wins on inputs & features. Sonar Pro 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-4 Turbo, Qwen Plus Character (Japanese) or Sonar Pro?
Qwen Plus Character (Japanese) is cheaper at $0.50 input / $1.40 output per million tokens (official Alibaba API price). Sonar Pro costs $3.00 input / $15.00 output per million tokens (official Perplexity API price); GPT-4 Turbo costs $10.00 input / $30.00 output per million tokens (official OpenAI 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 $6.00 for Sonar Pro (8.3× as much) and $15.00 for GPT-4 Turbo (21× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-4 Turbo has not been scored yet, Qwen Plus Character (Japanese) has not been scored yet and Sonar Pro has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-4 Turbo, Qwen Plus Character (Japanese) and Sonar Pro yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Sonar Pro does not support tool calling, which most coding agents need.
Which has the bigger context window?
Sonar Pro has the largest context window at 200,000 tokens, against 128,000 for GPT-4 Turbo and 8,192 for Qwen Plus Character (Japanese). Maximum output per response: GPT-4 Turbo up to 4,096, Qwen Plus Character (Japanese) up to 512, Sonar Pro up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GPT-4 Turbo accepts text and images; Qwen Plus Character (Japanese) accepts text; Sonar Pro accepts text and images. GPT-4 Turbo handles the widest range of inputs.
Are any of these open source?
No. GPT-4 Turbo, Qwen Plus Character (Japanese) and Sonar Pro are proprietary and only available through APIs and apps.
Which is newer?
Qwen Plus Character (Japanese) is the newest, released Jan 2024. Sonar Pro came out Jan 1, 2024; GPT-4 Turbo came out Nov 6, 2023. Knowledge cutoff: GPT-4 Turbo Dec 2023, Qwen Plus Character (Japanese) Apr 2024, Sonar Pro Sep 1, 2025.
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.
