Ling 3.0 flash vs Qwen3.5 122B-A10B
Ling 3.0 flash scores higher on the intelligence index, 25 against 16. Both take the same machine to start: the cheapest here that runs either is the Framework Desktop, 128GB, at $3,449. On it, Ling 3.0 flash is about 2.1× quicker: 41 tok/s against 20, both estimated from memory bandwidth. At 500k tokens a day the Framework Desktop, 128GB pays for itself in 53 years running Qwen3.5 122B-A10B, against 1,352 years running Ling 3.0 flash.
| Ling 3.0 flash | Qwen3.5 122B-A10B | |
|---|---|---|
| Intelligence index | 25 | 16 |
| Class | Haiku-class | Haiku-class |
| Weights | 78 GB | 78 GB |
| Needs at 32k | 78 GB | 79 GB |
| Quantisation | Q4_K_M | UD-Q4_K_M |
| Parameters | 124B (5.1B active) | 125.1B (10B active) |
| Max context | 256k | 256k |
| API price per 1M | $0.021 in / $0.063 out | $0.26 in / $2.08 out |
| Licence | MIT | Apache 2.0 |
| Machines here that run it | 12 of 37 | 12 of 37 |
| Cheapest machine that runs it | Strix Halo Framework Desktop, 128GB $3,449 | Strix Halo Framework Desktop, 128GB $3,449 |
Run Ling 3.0 flash on the Framework Desktop, 128GB · or Qwen3.5 122B-A10B
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.
| Ling 3.0 flash | Qwen3.5 122B-A10B | |
|---|---|---|
| Speed at 32k | 41 tok/s estimated | 20 tok/s estimated |
| Pay-back on this machine | Pays back in 1,352 years | Pays back in 53 years |
| API cost per month | $0.36 | $5.69 |
Run Ling 3.0 flash on the Framework Desktop, 128GB · or Qwen3.5 122B-A10B
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. Qwen3.5 122B-A10B 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 | Ling 3.0 flash | Qwen3.5 122B-A10B |
|---|---|---|
| 50ka few chats a day | 13,521 years | 533 years |
| 200klight assistant use | 3,380 years | 133 years |
| 1Ma moderate coding-assistant day | 676 years | 27 years |
| 4Mheavy coding with an agent | 169 years | 6.7 years |
| 20Magents running most of the day | 34 years | 16 months |
Run Ling 3.0 flash at 20M tokens a day · or Qwen3.5 122B-A10B
Memory is not what separates them
Ling 3.0 flash needs 78 GB of memory at 32k of context and Qwen3.5 122B-A10B needs 79 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.
Ling 3.0 flash is also head to head with Tencent Hy3 above it on the leaderboard and MiniMax M2.7 below it. One more model needs much the same memory: Mistral Small 4 (119B-2603). Qwen3.5 122B-A10B is also head to head with Gemma 4 26B-A4B above it on the leaderboard and Gemma 4 31B it below it. One more model needs much the same memory: GLM-4.5-Air.
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 Ling 3.0 flash · every machine that runs Qwen3.5 122B-A10B · every other match-up · both against the frontier · the quickest pay-back at each level of use