OLMo 2 7B: pricing, context window and benchmarks
OLMo 2 7B is a small open-weight model from Allen Institute for AI. It offers a 16,384-token context window, costs $0.56 per million input tokens and $2.58 per million output tokens, and averages 51.7 across the index's benchmark suite.
Pricing
OLMo 2 7B costs $0.56 per million input tokens and $2.58 per million output tokens. A blended 3:1 input-to-output mix works out to about $1.06 per million tokens. Cached input is listed at $0.06 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $0.56 | per 1M tokens |
| Output | $2.58 | per 1M tokens |
| Cached input | $0.06 | per 1M tokens |
| Blended 3:1 | $1.06 | estimate per 1M tokens |
| 1M in + 1M out | $3.14 | estimate |
Benchmarks
Scores below are modelled estimates on a 0–100 scale. Use them to compare relative position, not as official vendor results.
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 59.3 | 533 | 11th | |
| GPQA Diamond | 48.2 | 536 | 11th | |
| SWE-bench Verified | 35.5 | 552 | 8th | |
| HumanEval | 55.9 | 536 | 11th | |
| MATH-500 | 49.0 | 563 | 6th | |
| AIME 2025 | 41.2 | 535 | 11th | |
| LiveCodeBench | 40.3 | 536 | 11th | |
| IFEval | 63.4 | 544 | 9th | |
| TruthfulQA | 59.1 | 540 | 10th | |
| HellaSwag | 67.0 | 564 | 6th | |
| MMMU | 50.1 | 553 | 8th | |
| Arena Elo | 1211.0 | 543 | 10th |
What OLMo 2 7B is good for
OLMo 2 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $0.56 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $0.56 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
Trade-offs
- Context window of 16,384 tokens is small for long-document work.
- Average benchmark score is below the index median; test it on your own task.
- Less reliable on open-ended reasoning and complex multi-step instructions.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Llama 3.3 1.5B | Meta AI | 4096 | $0.61 | 52.2 |
| DBRX 7B | Databricks | 8192 | $0.65 | 51.9 |
| OLMo 2 7B | Allen Institute for AI | 16384 | $0.56 | 51.7 |
| QwQ 1.5B | Alibaba Cloud | 32768 | $0.61 | 51.6 |
| MiniMax-Text 1.5B | MiniMax | 32768 | $0.58 | 51.5 |
| Phi-3.5 8B | Microsoft | 16384 | $0.59 | 51.2 |
FAQ
How much does OLMo 2 7B cost?
OLMo 2 7B is listed at $0.56 per million input tokens and $2.58 per million output tokens. Cached input is $0.063 per million tokens. A blended 3:1 input-to-output mix is about $1.06 per million tokens.
What is the context window of OLMo 2 7B?
OLMo 2 7B has a context window of 16,384 tokens and can emit up to 16,384 output tokens per request in this index.
Is OLMo 2 7B open weights?
Yes. OLMo 2 7B is listed as an open-weight model under a open weights licence.
How does OLMo 2 7B score on benchmarks?
OLMo 2 7B averages 51.7 across the scored benchmarks in this index, ranking #543 of 600 models. Its strongest relative result is Arena Elo at 1211.0.
What is OLMo 2 7B best for?
OLMo 2 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $0.56 per million input tokens it can handle a large share of production traffic.