What we measured MiniMax M3 lists at roughly one tenth of Claude Opus 4.8's price per token, so the question every team asks is simple: if you send your traffic to the cheaper model, what do you actually give up? We ran both models on 198 real user prompts and measured the four things that matter in production, cost savings, accuracy, error rate, and speed. Here is what we found. | Metric | MiniMax M3 vs Opus 4.8 | |---|---| | Cost | ~8x cheaper per answer | | Accuracy | Within 1 point of Opus on 70% of prompts | | Error rate | 27% of answers clearly worse (a severe drop) | | Latency | ~1.6x faster (7.0s vs 11.3s median) | Cost savings At list prices MiniMax M3 is about ten times cheaper than Opus 4.8 per token. On our 198 prompts it cost about $0.54 against roughly $4.24 for Opus, close to eight times less. The per-answer saving is nearer 8x than 10x because MiniMax is a reasoning model that writes out its thinking, so it spends more output tokens per answer than Opus does. Either way, the cost difference is large and real. Accuracy To measure accuracy we had both models answer the same prompt and scored each answer on a five-point quality scale. MiniMax landed within one point of Opus on 70% of prompts overall, and produced a genuinely good answer (4 out of 5 or better) on 56% of them. How well it holds up depends heavily on how hard the prompt is: | Difficulty | Answers within 1 point of Opus | |---|---| | Simple | 77% | | Medium | 81% | | Complex | 40% | On everyday questions MiniMax keeps pace with Opus about four times out of five. On genuinely hard prompts it falls well behind. Error rate An average hides the risk that actually matters: how often the cheaper model returns an answer a user would notice is clearly worse. We count that as a severe drop, two or more points below Opus. Across all prompts the severe-drop rate was 27%, and it more than triples with difficulty: | Difficulty | Severe drops | |---|---| | Simple | 18% | | Medium | 19% | | Complex | 58% | Severe-drop rate by prompt difficulty: MiniMax M3 produces a clearly worse answer than Opus 4.8 on 18% of simple prompts, 19% of medium prompts, and 58% of complex prompts, more than tripling with difficulty. Alongside, cost for 198 prompts: about $0.54 for MiniMax against $4.24 for Opus, roughly eight times cheaper per answer. The takeaway is the shape of the risk. Even easy prompts fail severely about one time in six, and you cannot always tell which. A short factual question can trip it up while a longer, more involved one sails through, so the failures are not something you can eyeball in advance. Latency MiniMax M3 is also faster, not slower, despite writing out its reasoning. On the same prompts it returned a complete answer in a median of 7.0 seconds against 11.3 seconds for Opus, roughly 1.6 times quicker. What this means for you MiniMax M3 is a strong, fast, inexpensive model. On the majority of everyday prompts it stays within a point of Opus, costs about eight times less, and answers noticeably quicker. The catch is the tail: roughly a quarter of a wholesale swap comes back clearly worse, concentrated on harder work and rising to well over half on complex prompts. Replacing Opus everywhere is therefore a real quality risk. The safe way to capture the savings is to send easy, low-risk traffic to the cheaper model and keep hard traffic on the frontier model, rather than switching wholesale. How we tested 198 real prompts sampled from an open dataset of anonymized chat conversations, spanning factual questions, coding, math, creative writing, and everyday assistant tasks. Each prompt was answered by MiniMax M3 and Claude Opus 4.8, and the two answers were scored blind on quality. Cost uses public list prices; latency is the measured time to a complete answer. This is a directional comparison across one prompt set, not an exhaustive benchmark.