GPT-4 Turbo vs Qwen2.5 72B Instruct vs Sonar Pro
Qwen2.5 72B Instruct comes out ahead, 28 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)Qwen2.5 72B Instruct
28/100- ECI129.0
- Price$1.40 / $5.60
- Context131K
PerplexitySonar Pro
20/100- ECI—
- Price$3.00 / $15.00
- Context200K
Qwen2.5 72B Instruct is our pick
Qwen2.5 72B Instruct is the better all-round choice, scoring 28/100 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 priceQwen2.5 72B InstructQwen2.5 72B Instruct $2.45 · Sonar Pro $6.00 · GPT-4 Turbo $15.00 per 1M tokens (3:1 blend)
- Longest contextSonar ProSonar Pro 200,000 · Qwen2.5 72B Instruct 131,072 · GPT-4 Turbo 128,000 tokens
- Widest inputsGPT-4 Turbo and Sonar ProGPT-4 Turbo: Text, Images · Qwen2.5 72B Instruct: Text · Sonar Pro: Text, Images
- Self-hostingQwen2.5 72B InstructPublishes downloadable weights
| Measure | Weight | GPT-4 Turbo | Qwen2.5 72B Instruct | Sonar Pro |
|---|---|---|---|---|
| Price | 50% | 0 | 31 | 13 |
| Inputs & features | 30% | 50 | 25 | 25 |
| Context window | 20% | 24 | 24 | 32 |
| Overall | 100% | 20/100 | 28/100 | 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 | Qwen2.5 72B InstructAlibaba (Qwen) | Sonar ProPerplexity |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 129.0 | — |
| ECI rank | — | #128 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 49.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 8.1% | — |
| Price per million tokens | |||
| Input | $10.00 | $1.40 (best) | $3.00 |
| Output | $30.00 | $5.60 (best) | $15.00 |
| Cached input | — | — | — |
| Blended (3:1) | $15.00 | $2.45 (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 | 131,072 tokens | 200,000 tokens (best) |
| Max output | 4,096 tokens | 8,192 tokens (best) | 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 | Open | Proprietary |
| API model ID | gpt-4-turbo | qwen2-5-72b-instruct | sonar-pro |
| API providers | 12 (best) | 1 | 5 |
| Released | Nov 6, 2023 | Sep 19, 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
- Qwen2.5 72B Instruct$25.20
- Sonar Pro$60.00
Which should you choose?
Which is better: GPT-4 Turbo, Qwen2.5 72B Instruct or Sonar Pro?
Qwen2.5 72B Instruct is the better all-round choice, scoring 28/100 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, Qwen2.5 72B Instruct or Sonar Pro?
Qwen2.5 72B Instruct is cheaper at $1.40 input / $5.60 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 $2.45 per million tokens for Qwen2.5 72B Instruct versus $6.00 for Sonar Pro (2.4× as much) and $15.00 for GPT-4 Turbo (6.1× 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, Qwen2.5 72B Instruct has an ECI of 129.0 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, Qwen2.5 72B Instruct 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 131,072 for Qwen2.5 72B Instruct and 128,000 for GPT-4 Turbo. Maximum output per response: GPT-4 Turbo up to 4,096, Qwen2.5 72B Instruct up to 8,192, Sonar Pro up to 8,192 tokens.
Which can read images, PDFs, audio or video?
GPT-4 Turbo accepts text and images; Qwen2.5 72B Instruct accepts text; Sonar Pro accepts text and images. GPT-4 Turbo handles the widest range of inputs.
Are any of these open source?
Qwen2.5 72B Instruct publishes its weights and can be self-hosted; GPT-4 Turbo and Sonar Pro is proprietary.
Which is newer?
Qwen2.5 72B Instruct is the newest, released Sep 19, 2024. Sonar Pro came out Jan 1, 2024; GPT-4 Turbo came out Nov 6, 2023. Knowledge cutoff: GPT-4 Turbo Dec 2023, Qwen2.5 72B Instruct 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.
