Qwen3-Coder Next vs gpt-oss-20b
Qwen3-Coder Next scores higher on the intelligence index, 10 against 9. The cheapest machine here that runs Qwen3-Coder Next is the Framework Desktop, 128GB, at $3,449. gpt-oss-20b runs on the Mac mini M6, 24GB at $1,099, $2,350 less. On the Framework Desktop, 128GB, the cheapest machine here that runs both, Qwen3-Coder Next is about 1.5× quicker: 54 tok/s against 37, gpt-oss-20b's measured and the other estimated from memory bandwidth. At 500k tokens a day the Framework Desktop, 128GB pays for itself in 122 years running Qwen3-Coder Next, against 1,602 years running gpt-oss-20b.
| Qwen3-Coder Next | gpt-oss-20b | |
|---|---|---|
| Intelligence index | 10 | 9 |
| Class | Below every hosted tier | Below every hosted tier |
| Weights | 48 GB | 12 GB |
| Needs at 32k | 49 GB | 13 GB |
| Quantisation | Q4_K_M | MXFP4 |
| Parameters | 79.7B (3B active) | 20.9B (3.6B active) |
| Max context | 256k | 128k |
| API price per 1M | $0.12 in / $0.8 out | $0.018 in / $0.09 out |
| Licence | Apache 2.0 | Apache 2.0 |
| Machines here that run it | 13 of 37 | 33 of 37 |
| Cheapest machine that runs it | Strix Halo Framework Desktop, 128GB $3,449 | Mac mini M6, 24GB $1,099 |
Run Qwen3-Coder Next on the Framework Desktop, 128GB · or gpt-oss-20b on the Mac mini M6, 24GB
Jump to: Side by side on the Framework Desktop, 128GB · How much use it takes to pay for the machine · Machines that run one and not the other · The assumptions behind both columns
Side by side on the Framework Desktop, 128GB
The table above gives each model the cheapest machine that runs it, and those are two different machines, so nothing in it is a like-for-like race. The Strix Halo Framework Desktop, 128GB is the cheapest machine here that runs both, so this is the pair doing the same work on the same hardware.
| Qwen3-Coder Next | gpt-oss-20b | |
|---|---|---|
| Speed at 32k | 54 tok/s estimated | 37 tok/s measured |
| Pay-back on this machine | Pays back in 122 years | Pays back in 1,602 years |
| API cost per month | $2.47 | $0.34 |
Run Qwen3-Coder Next on the Framework Desktop, 128GB · or gpt-oss-20b
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. Qwen3-Coder Next 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 | Qwen3-Coder Next | gpt-oss-20b |
|---|---|---|
| 50ka few chats a day | 1,217 years | 16,019 years |
| 200klight assistant use | 304 years | 4,005 years |
| 1Ma moderate coding-assistant day | 61 years | 801 years |
| 4Mheavy coding with an agent | 15 years | 200 years |
| 20Magents running most of the day | 3.0 years | 40 years |
Run Qwen3-Coder Next at 20M tokens a day · or gpt-oss-20b
Machines that run one and not the other
gpt-oss-20b needs 13 GB of memory at 32k of context and Qwen3-Coder Next needs 49 GB. That puts gpt-oss-20b on 20 of the 37 machines priced here that Qwen3-Coder Next does not, starting at $1,099.
| Machine | Price | Memory | Speed on gpt-oss-20b | Pay-back |
|---|---|---|---|---|
| Mac mini M6, 24GB | $1,099 | 24 GB | 18 tok/s estimated | Pays back in 518 years |
| Strix Halo Framework Desktop, 32GB | $1,269 | 32 GB | 49 tok/s estimated | Pays back in 481 years |
| Mac mini M6, 32GB | $1,299 | 32 GB | 18 tok/s estimated | Pays back in 612 years |
| AMD Radeon AI PRO R9700, 32GB | $1,299card only | 32 GB | 103 tok/s measured | Pays back in 513 years |
| Mac mini M5 Pro, 24GB | $1,699 | 24 GB | 32 tok/s estimated | Pays back in 977 years |
| Strix Halo Corsair AI Workstation 300, 64GB | $1,700 | 64 GB | 49 tok/s estimated | Pays back in 645 years |
14 more, on the gpt-oss-20b page.
Qwen3-Coder Next is also head to head with Qwen3-Coder 30B-A3B above it on the leaderboard. One more model needs much the same memory: DeepSeek-R1-Distill-Llama-70B. gpt-oss-20b is also head to head with Gemma 4 E4B below it on the leaderboard. One more model needs much the same memory: Ministral 3 14B.
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 Qwen3-Coder Next · every machine that runs gpt-oss-20b · every other match-up · both against the frontier · the quickest pay-back at each level of use