GPT-4o vs Command R+
GPT-4o comes out ahead, 35 to 25 on our weighted score.
- Our pick
OpenAIGPT-4o
35/100- ECI129.0
- Price$2.50 / $10.00
- Context128K
CohereCommand R+
25/100- ECI—
- Price$2.50 / $10.00
- Context128K
Add a model
Make it a three-way comparison.
GPT-4o is our pick
GPT-4o is the better all-round choice, scoring 35/100 against Command R+ (25). It leads on inputs & features. 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 priceSame priceGPT-4o $4.38 · Command R+ $4.38 per 1M tokens (3:1 blend)
- Longest contextAbout the sameGPT-4o 128,000 · Command R+ 128,000 tokens
- Widest inputsGPT-4oGPT-4o: Text, Images, PDFs · Command R+: Text
- Self-hostingCommand R+Publishes downloadable weights
| Measure | Weight | GPT-4o | Command R+ |
|---|---|---|---|
| Price | 50% | 19 | 19 |
| Inputs & features | 30% | 70 | 35 |
| Context window | 20% | 24 | 24 |
| Overall | 100% | 35/100 | 25/100 |
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-4oOpenAI | Command R+Cohere |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 129.0 | — |
| ECI rank | #141 of 165 | — |
| GPQA DiamondGraduate-level science questions | 48.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 6.3% | — |
| Price per million tokens | ||
| Input | $2.50 | $2.50 |
| Output | $10.00 | $10.00 |
| Cached input | $1.25 | — |
| Blended (3:1) | $4.38 | $4.38 |
| Long-context rate | Same rate | Same rate |
| Price source | Official OpenAI API | Official Cohere API |
| Limits | ||
| Context window | 128,000 tokens | 128,000 tokens |
| Max output | 16,384 tokens (best) | 4,000 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | Yes | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | gpt-4o | command-r-plus-08-2024 |
| API providers | 26 (best) | 8 |
| Released | May 13, 2024 | Aug 30, 2024 |
| Knowledge cutoff | Sep 2023 | Jun 1, 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-4o$45.00
- Command R+$45.00
Which should you choose?
Which is better: GPT-4o or Command R+?
GPT-4o is the better all-round choice, scoring 35/100 against Command R+ (25). It leads on inputs & features. 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-4o or Command R+?
GPT-4o and Command R+ cost the same: $2.50 input / $10.00 output per million tokens.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. GPT-4o has an ECI of 129.0 and Command R+ has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-4o and Command R+ yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
GPT-4o and Command R+ share the same 128,000-token context window. Maximum output per response: GPT-4o up to 16,384, Command R+ up to 4,000 tokens.
Which can read images, PDFs, audio or video?
GPT-4o accepts text, images and PDFs; Command R+ accepts text. GPT-4o handles the widest range of inputs.
Are any of these open source?
Command R+ publishes its weights and can be self-hosted; GPT-4o is proprietary.
Which is newer?
Command R+ is the newest, released Aug 30, 2024. GPT-4o came out May 13, 2024. Knowledge cutoff: GPT-4o Sep 2023, Command R+ Jun 1, 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 Nov 2, 2026.
