o1-pro vs ALLaM-2-7b
o1-pro comes out ahead, 55 to 0 on our weighted score, and it is the cheaper option too.
- Our pick
OpenAIo1-pro
55/100- ECI—
- Price$150.00 / $600.00
- Context200K
SDAIA
ALLaM-2-7b
0/100- ECI—
- Price—
- Context4K
Add a model
Make it a three-way comparison.
o1-pro is our pick
o1-pro is the better all-round choice, scoring 55/100 against ALLaM-2-7b (0). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 priceo1-proo1-pro $262.50 per 1M tokens (3:1 blend) · ALLaM-2-7b unpriced
- Longest contexto1-proo1-pro 200,000 · ALLaM-2-7b 4,096 tokens
- Widest inputso1-proo1-pro: Text, Images · ALLaM-2-7b: Text
- Self-hostingALLaM-2-7bPublishes downloadable weights
| Measure | Weight | o1-pro | ALLaM-2-7b |
|---|---|---|---|
| Inputs & features | 60% | 70 | 0 |
| Context window | 40% | 32 | 0 |
| Overall | 100% | 55/100 | 0/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | o1-proOpenAI | ALLaM-2-7bSDAIA |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $150.00 | — |
| Output | $600.00 | — |
| Cached input | — | — |
| Blended (3:1) | $262.50 | — |
| Long-context rate | Same rate | — |
| Price source | Official OpenAI API | — |
| Limits | ||
| Context window | 200,000 tokens (best) | 4,096 tokens |
| Max output | 100,000 tokens (best) | 4,096 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yeslow · medium · high | No |
| Tool calling | Yes | No |
| Structured output | Yes | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | o1-pro | — |
| API providers | 5 (best) | 2 |
| Released | Mar 19, 2025 | Jan 23, 2025 |
| Knowledge cutoff | Sep 2023 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o1-pro$2,700
- ALLaM-2-7b—
Which should you choose?
Which is better: o1-pro or ALLaM-2-7b?
o1-pro is the better all-round choice, scoring 55/100 against ALLaM-2-7b (0). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, o1-pro or ALLaM-2-7b?
o1-pro is cheaper at $150.00 input / $600.00 output per million tokens (official OpenAI API price). . At a typical mix of three input tokens to one output token, that is $262.50 per million tokens for o1-pro versus . ALLaM-2-7b has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. o1-pro has not been scored yet and ALLaM-2-7b has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for o1-pro and ALLaM-2-7b yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that ALLaM-2-7b does not support tool calling, which most coding agents need.
Which has the bigger context window?
o1-pro has the largest context window at 200,000 tokens, against 4,096 for ALLaM-2-7b. Maximum output per response: o1-pro up to 100,000, ALLaM-2-7b up to 4,096 tokens.
Which can read images, PDFs, audio or video?
o1-pro accepts text and images; ALLaM-2-7b accepts text. o1-pro handles the widest range of inputs.
Are any of these open source?
ALLaM-2-7b publishes its weights and can be self-hosted; o1-pro is proprietary.
Which is newer?
o1-pro is the newest, released Mar 19, 2025. ALLaM-2-7b came out Jan 23, 2025. Knowledge cutoff: o1-pro Sep 2023.
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.
