MiniMax M3 vs GPT-5.2 Pro
MiniMax M3 and GPT-5.2 Pro sit at opposite ends of the pricing spectrum. M3 is a multimodal (text, image, video) model from MiniMax with a 1M-token context window, released 2026-05-31. GPT-5.2 Pro is OpenAI's reasoning-focused flagship, released 2025-12-10, with mandatory reasoning at medium/high/xhigh effort and a 400K context window. The gap that matters isn't features — it's price: M3's input tokens cost 1% of GPT-5.2 Pro's per the computed input price ratio.
Spec vs spec
| Spec | MiniMax M3 | GPT-5.2 Pro |
|---|---|---|
| Context window | 1.0M | 400K |
| Max output | 512K | 128K |
| Input modalities | text, image, video | image, text, file |
| Output modalities | text | text |
| Released | May 31, 2026 | Dec 10, 2025 |
| Reasoning | optional | always on |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
minimax/minimax-m3
Input · 1M tokens
$0.300 + 3%$0.309
Output · 1M tokens
$1.20 + 3%$1.24
Cache read · 1M tokens
$0.060 + 3%$0.062
FEE — FLAT, EVERY MODEL3%
openai/gpt-5.2-pro
Input · 1M tokens
$21.00 + 3%$21.63
Output · 1M tokens
$168.00 + 3%$173.04
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
minimax/minimax-m3Cheaper
$5.56
$5.40 provider + 3%
openai/gpt-5.2-pro
$562.38
$546.00 provider + 3%
Pricing math on a real workload
Take a 10M-input / 2M-output token job — a realistic batch or long-agent-run size. On provider list pricing, MiniMax M3 charges $0.30/M input and $1.20/M output, landing at $5.40 total for that workload. GPT-5.2 Pro charges $21.00/M input and $168.00/M output, landing at $546.00 for the same job — a 101x difference. On OpenKey the flat 3% fee applies to both: M3 becomes $0.309/M input and $1.236/M output (0.30 × 1.03, 1.20 × 1.03); GPT-5.2 Pro becomes $21.63/M input and $173.04/M output (21.00 × 1.03, 168.00 × 1.03). Both models run through the same OpenKey key, so switching between them for cost-sensitive vs. quality-critical calls doesn't require separate provider accounts.
Context and output ceiling
M3's context window is 1,048,576 tokens, 2.62x GPT-5.2 Pro's 400,000 — that's the computed context ratio. M3 also allows up to 512,000 completion tokens per call versus 128,000 for GPT-5.2 Pro. If your workload is a large codebase dump, a long document corpus, or a multi-hour agent trace that needs to stay in one context, M3 has more headroom on both sides of the request. GPT-5.2 Pro's smaller ceiling is less of a constraint for shorter, reasoning-heavy single tasks, where the model's effort settings matter more than raw window size.
Reasoning and agentic behavior
GPT-5.2 Pro makes reasoning mandatory — you can't turn it off — with three effort levels (medium, high, xhigh) and medium as the default. That's a deliberate trade: the model always spends tokens thinking before answering, which is part of why completion tokens cost so much more. M3's reasoning is optional (supports `reasoning` as a parameter but doesn't require it), which gives you a lever to cut latency and cost on simpler calls. On artificial_analysis metrics, M3 scores an intelligence index of 44.4, a coding index of 58.6, and an agentic index of 35.4 — useful as a coarse signal that it's built with coding and agent workloads specifically in mind, though there's no equivalent artificial_analysis score published for GPT-5.2 Pro to compare directly.
Modality differences
M3 takes text, image, and video as input and returns text — the video input support is notable since it's not universal even among newer multimodal models. GPT-5.2 Pro takes text, image, and file input, also outputting text only. If your pipeline needs to hand a model raw video, M3 is the one that accepts it natively; if you're feeding structured files (PDFs, docs) rather than video, GPT-5.2 Pro's file modality covers that case directly.
When to pick each
Default to MiniMax M3 for anything with high call volume, long context requirements, or a tight budget — coding assistants, agent loops, batch document processing. Reach for GPT-5.2 Pro when a single task is hard enough that you want OpenAI's mandatory, tunable reasoning depth (up to xhigh effort) and cost per call is a rounding error against the value of getting the answer right the first time. Don't use GPT-5.2 Pro for high-throughput loops — at $546.00 per 10M-in/2M-out workload versus $5.40, the bill scales fast.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume coding assistant | MiniMax M3 | $5.40 vs $546.00 for a 10M-in/2M-out workload — cost scales linearly with call volume |
| Long-document or large-codebase analysis | MiniMax M3 | 1,048,576-token context is 2.62x GPT-5.2 Pro's 400,000 |
| Single hard reasoning task, cost secondary | GPT-5.2 Pro | mandatory reasoning with xhigh effort tier, not available on M3 |
| Video input processing | MiniMax M3 | M3 accepts video as an input modality; GPT-5.2 Pro does not |
| File-based document input (PDFs, docs) | GPT-5.2 Pro | file is a listed input modality for GPT-5.2 Pro |
| Long agent runs needing large output | MiniMax M3 | 512,000 max completion tokens vs 128,000 for GPT-5.2 Pro |
Questions
- How much cheaper is MiniMax M3 than GPT-5.2 Pro?
- For a 10M-input/2M-output workload, MiniMax M3 costs $5.40 on provider pricing while GPT-5.2 Pro costs $546.00 — roughly 101x more. The input price ratio alone is 0.01, meaning M3's per-token input cost is about 1% of GPT-5.2 Pro's.
- Which model has the bigger context window?
- MiniMax M3 supports 1,048,576 tokens of context, compared to 400,000 for GPT-5.2 Pro — a 2.62x ratio per the computed context comparison. M3 also allows a larger max completion of 512,000 tokens vs 128,000 for GPT-5.2 Pro.
- Does GPT-5.2 Pro always use reasoning?
- Yes — reasoning is mandatory for GPT-5.2 Pro, with three supported effort levels: medium (default), high, and xhigh. MiniMax M3 supports a reasoning parameter but doesn't require it.
- Can I use both models with the same API key?
- Yes. Both MiniMax M3 and GPT-5.2 Pro are available on OpenKey with one key across all models, and pricing on both is the provider's list price plus a flat 3% fee — for example M3's $0.30/M input becomes $0.309/M on OpenKey.