DeepSeek-R1-Distill-Llama-70B vs Llama 3.3 70B Instruct
DeepSeek-R1-Distill-Llama-70B and Llama 3.3 70B Instruct both score 8 on the intelligence index. Both take the same machine to start: the cheapest here that runs either is the Framework Desktop, 128GB, at $3,449. On it, they run at much the same speed: 4 and 4 tok/s, both measured. At 500k tokens a day the Framework Desktop, 128GB pays for itself in 1,274 years running Llama 3.3 70B Instruct, and never running DeepSeek-R1-Distill-Llama-70B.
| DeepSeek-R1-Distill-Llama-70B | Llama 3.3 70B Instruct | |
|---|---|---|
| Intelligence index | 8 | 8 |
| Class | Below every hosted tier | Below every hosted tier |
| Weights | 43 GB | 43 GB |
| Needs at 32k | 53 GB | 53 GB |
| Quantisation | Q4_K_M | Q4_K_M |
| Parameters | 70.6B | 70.6B |
| Max context | 128k | 128k |
| API price per 1M | $0.018 in / $0.09 outpriced as gpt-oss-20b | $0.1 in / $0.32 out |
| Licence | MIT | Llama 3.3 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 | usable | good |
| Translation | usable | good |
| Everyday coding | usable | good |
| Reasoning & maths | good | usable |
| Agentic work | don’t | usable |
Run DeepSeek-R1-Distill-Llama-70B on the Framework Desktop, 128GB · or Llama 3.3 70B Instruct
Ratings are coarse on purpose: they say what a model is usable for, not where it places to the decimal.
Jump to: Side by side on the Framework Desktop, 128GB · How much use it takes to pay for the machine · Memory is not what separates them · The assumptions behind both columns
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.
| DeepSeek-R1-Distill-Llama-70B | Llama 3.3 70B Instruct | |
|---|---|---|
| Speed at 32k | 4 tok/s measured | 4 tok/s measured |
| Pay-back on this machine | Never pays back | Pays back in 1,274 years |
| API cost per month | $0.34priced as gpt-oss-20b | $1.73 |
Run DeepSeek-R1-Distill-Llama-70B on the Framework Desktop, 128GB · or Llama 3.3 70B Instruct
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 3.3 70B Instruct pays for it sooner at every level of use: DeepSeek-R1-Distill-Llama-70B never pays for it at all.
| A day's use | DeepSeek-R1-Distill-Llama-70B | Llama 3.3 70B Instruct |
|---|---|---|
| 50ka few chats a day | Never pays back | 12,740 years |
| 200klight assistant use | Never pays back | 3,185 years |
| 1Ma moderate coding-assistant day | Never pays back | 637 years |
| 4Mheavy coding with an agent | Never pays back | 159 years |
| 20Magents running most of the day | Never pays backits ceiling | 116 yearsits ceiling |
The Framework Desktop, 128GB cannot generate 20M tokens a day on either model: at most 5.49M on DeepSeek-R1-Distill-Llama-70B and 5.49M on Llama 3.3 70B Instruct. Both figures on that row are for the most it can do.
Run DeepSeek-R1-Distill-Llama-70B at 20M tokens a day · or Llama 3.3 70B Instruct
Memory is not what separates them
DeepSeek-R1-Distill-Llama-70B needs 53 GB of memory at 32k of context and Llama 3.3 70B Instruct needs 53 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.
DeepSeek-R1-Distill-Llama-70B is also head to head with DeepSeek-R1-Distill-Qwen-32B above it on the leaderboard. One more model needs much the same memory: Qwen3-Coder Next. Llama 3.3 70B Instruct is also head to head with LFM2.5 2.6B below it on the leaderboard.
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 DeepSeek-R1-Distill-Llama-70B · every machine that runs Llama 3.3 70B Instruct · every other match-up · both against the frontier · the quickest pay-back at each level of use