MiniMax M3 vs GPT-5.2
MiniMax M3 and GPT-5.2 sit at opposite ends of the price-performance curve. M3 is a multimodal (text, image, video) model from MiniMax with a 1,048,576-token context window and provider pricing of $0.30/$1.20 per million tokens. GPT-5.2 is OpenAI's frontier model with adaptive reasoning effort levels, a 400,000-token context window, and provider pricing of $1.75/$14.00 per million tokens. Both run on OpenKey with one key and a flat 3% fee on top of provider list price.
Spec vs spec
| Spec | MiniMax M3 | GPT-5.2 |
|---|---|---|
| Context window | 1.0M | 400K |
| Max output | 512K | 128K |
| Input modalities | text, image, video | file, image, text |
| Output modalities | text | text |
| Released | May 31, 2026 | Dec 10, 2025 |
| Reasoning | optional | optional |
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
Input · 1M tokens
$1.75 + 3%$1.80
Output · 1M tokens
$14.00 + 3%$14.42
Cache read · 1M tokens
$0.175 + 3%$0.180
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
$46.87
$45.50 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| MiniMax M3 | GPT-5.2 | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| 3D | 1306 | #16 | 1156 | #60 |
| androidnative | 990 | #27 | 1074 | #22 |
| ASCII art | 1219 | #15 | 1199 | #22 |
| Code | 1306 | #13 | 1219 | #42 |
| Data viz | 1295 | #11 | 1245 | #31 |
| Game dev | 1287 | #20 | 1260 | #29 |
| SVG | 1250 | #13 | 1197 | #31 |
| UI components | 1291 | #18 | 1243 | #32 |
| Websites | 1304 | #11 | 1237 | #34 |
Head-to-head preference voting. How we filter and rank
Pricing math on a real workload
For a 10M input / 2M output token job: MiniMax M3 costs $5.40 total on OpenKey (provider price $0.309/$1.236 per million with the 3% fee applied: 0.3 × 1.03 = $0.309, 1.2 × 1.03 = $1.236). GPT-5.2 costs $45.50 for the same workload (provider $1.75 × 1.03 = $1.8025 input, $14.00 × 1.03 = $14.42 output per million). That's an 8.4x cost difference for identical token counts. If you're running batch jobs, evals, or high-volume agent loops, this gap compounds fast — M3's input price alone is 0.17x GPT-5.2's.
Coding and agent benchmarks
On Design Arena's codecategories benchmark, MiniMax M3 scores 1306 elo (rank 13, 55.6% win rate) versus GPT-5.2's 1219 elo (rank 42, 50.2% win rate) — M3 comes out ahead here. GPT-5.2 has benchmark data across five agent categories (androidnative, fullstack, godotgamedev, mobileapps, webapps) that M3 doesn't report outside androidnative, where M3 scores 990 elo (rank 27) against GPT-5.2's 1074 elo (rank 22). Artificial Analysis puts M3's coding index at 58.6 and agentic index at 35.4 — no equivalent AA figures exist for GPT-5.2 in this data, so that comparison is one-sided.
Context and modality
MiniMax M3's context window is 1,048,576 tokens, 2.62x GPT-5.2's 400,000. M3 also allows up to 512,000 max completion tokens versus GPT-5.2's 128,000 — useful if you need long generated output, not just long input. On modality, M3 accepts text, image, and video input; GPT-5.2 accepts text, image, and file input but no video. Neither model outputs anything but text. For document-heavy or video-referencing agent pipelines, M3's larger window and video support matter more than raw benchmark elo.
Reasoning control
GPT-5.2 exposes five reasoning effort levels (xhigh, high, medium, low, none) with medium as default, giving you a dial to trade latency against quality per request. MiniMax M3 supports a reasoning parameter but isn't mandatory and doesn't expose graduated effort tiers in the same way. If your workload needs per-request control over how hard the model thinks — cheap and fast for simple calls, xhigh for the tough ones — GPT-5.2's effort parameter is the more direct lever.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume batch coding tasks | MiniMax M3 | 8.4x cheaper on the same 10M in / 2M out workload ($5.40 vs $45.50) and higher codecategories elo (1306 vs 1219) |
| Long-document or video-context analysis | MiniMax M3 | 1,048,576-token context window is 2.62x GPT-5.2's 400,000, plus native video input support |
| Full-stack or web-app agent building | GPT-5.2 | Only model with reported fullstack (1110 elo) and webapps (1156 elo) agent benchmark data |
| Variable-difficulty requests needing latency control | GPT-5.2 | Exposes five reasoning effort tiers (xhigh to none) to tune cost/speed per call |
| Mobile app agent development | GPT-5.2 | Reports a mobileapps benchmark score (1173 elo, rank 23) that MiniMax M3 doesn't have |
| Budget-constrained production pipelines | MiniMax M3 | Input tokens priced at 0.17x GPT-5.2's rate ($0.309 vs $1.8025 per million on OpenKey) |
Questions
- How much cheaper is MiniMax M3 than GPT-5.2?
- On a 10M input / 2M output token workload, MiniMax M3 costs $5.40 on OpenKey versus $45.50 for GPT-5.2 — roughly 8.4x less. The gap comes mostly from output pricing: $1.236 vs $14.42 per million tokens after the 3% OpenKey fee.
- Which model has a bigger context window?
- MiniMax M3 supports 1,048,576 tokens of context, 2.62x GPT-5.2's 400,000. M3 also allows up to 512,000 tokens of output compared to GPT-5.2's 128,000 max completion tokens.
- Does GPT-5.2 beat MiniMax M3 on coding benchmarks?
- Not on the codecategories benchmark reported here — M3 scores 1306 elo (rank 13) versus GPT-5.2's 1219 elo (rank 42). GPT-5.2 does have agent-specific benchmarks (fullstack, webapps, mobileapps) that M3 lacks.
- Can I use both models with one API key?
- Yes. Both MiniMax M3 and GPT-5.2 are available on OpenKey with a single key, and pricing is the provider's list price plus a flat 3% fee — no separate accounts or billing per provider.