gpt-oss-120b vs Llama 4 Scout 17B-16E
gpt-oss-120b scores higher on the intelligence index, 12 against 6. Both take the same machine to start: the cheapest here that runs either is the Framework Desktop, 128GB, at $3,449. On it, gpt-oss-120b is about 2.2× quicker: 35 tok/s against 16, both measured. At 500k tokens a day the Framework Desktop, 128GB pays for itself in 215 years running Llama 4 Scout 17B-16E, against 687 years running gpt-oss-120b.
| gpt-oss-120b | Llama 4 Scout 17B-16E | |
|---|---|---|
| Intelligence index | 12 | 6 |
| Class | Below every hosted tier | Below every hosted tier |
| Weights | 63 GB | 65 GB |
| Needs at 32k | 65 GB | 68 GB |
| Quantisation | MXFP4 | Q4_K_M |
| Parameters | 116.8B (5.1B active) | 108.6B (17B active) |
| Max context | 128k | 10240k |
| API price per 1M | $0.03 in / $0.17 out | $0.1 in / $0.3 out |
| Licence | Apache 2.0 | Llama 4 Community License |
| Machines here that run it | 13 of 37 | 13 of 37 |
| Cheapest machine that runs it | Strix Halo Framework Desktop, 128GB $3,449 | Strix Halo Framework Desktop, 128GB $3,449 |
| Summarising | good | good |
| Translation | usable | good |
| Everyday coding | good | usable |
| Reasoning & maths | good | usable |
| Agentic work | usable | usable |
Run gpt-oss-120b on the Framework Desktop, 128GB · or Llama 4 Scout 17B-16E
Ratings are coarse on purpose: they say what a model is usable for, not where it places to the decimal.
Side by side on the Framework Desktop, 128GB
The cheapest machine that runs either model is the same one, so this is the pair doing the same work on the same hardware: Strix Halo Framework Desktop, 128GB, at $3,449.
| gpt-oss-120b | Llama 4 Scout 17B-16E | |
|---|---|---|
| Speed at 32k | 35 tok/s measured | 16 tok/s measured |
| Pay-back on this machine | Pays back in 687 years | Pays back in 215 years |
| API cost per month | $0.59 | $1.71 |
Run gpt-oss-120b on the Framework Desktop, 128GB · or Llama 4 Scout 17B-16E
How much use it takes to pay for the machine
Everything above is at 500k tokens a day. Pay-back moves with how much you actually run, so here are both models at the five levels of use the calculator names, on the Framework Desktop, 128GB. Llama 4 Scout 17B-16E pays for it sooner at every level of use, so which of them to run does not turn on how hard you work it.
| A day's use | gpt-oss-120b | Llama 4 Scout 17B-16E |
|---|---|---|
| 50ka few chats a day | 6,875 years | 2,152 years |
| 200klight assistant use | 1,719 years | 538 years |
| 1Ma moderate coding-assistant day | 344 years | 108 years |
| 4Mheavy coding with an agent | 86 years | 27 years |
| 20Magents running most of the day | 17 years | 5.4 years |
Run gpt-oss-120b at 20M tokens a day · or Llama 4 Scout 17B-16E
Memory is not what separates them
gpt-oss-120b needs 65 GB of memory at 32k of context and Llama 4 Scout 17B-16E needs 68 GB. Every machine priced here that runs one runs the other, at 32k of context. So the choice between them is what each is good at, how fast it runs and what the same work costs on an API, not what you have to buy to hold it.
gpt-oss-120b is also head to head with Qwen3.5 4B above it on the leaderboard and Ling 3.0 tiny below it. Llama 4 Scout 17B-16E is also head to head with Qwen3 14B above it on the leaderboard and Ministral 3 14B below it.
The assumptions behind both columns
Both columns use the same usage: 500k tokens a day at 15:1 input to output, 32k of context, $0.17 per kWh, and today's API prices held flat. Speeds marked estimated are worked out from memory bandwidth rather than measured, and pay-back scales with them. Where nobody rents an open model by the token, its API prices are the nearest hosted model's, named beside them. Machines are the 37 here with a published price that are still sold. Change any of it in the calculator.
More head to head: every machine that runs gpt-oss-120b · every machine that runs Llama 4 Scout 17B-16E · every other match-up · both against the frontier · the quickest pay-back at each level of use