MiniMax M2.5 vs GPT-5 Mini
MiniMax M2.5 and GPT-5 Mini both target lightweight, high-throughput reasoning work, but they solve different problems. M2.5 is text-only, cheaper, and scores higher across every Design Arena category both models share. GPT-5 Mini accepts images and files, holds a larger context window, and carries OpenAI's tool-calling and structured-output stack. The choice comes down to whether your workload needs multimodal input and long context, or raw coding output at low cost.
Spec vs spec
| Spec | MiniMax M2.5 | GPT-5 Mini |
|---|---|---|
| Context window | 205K | 400K |
| Max output | 197K | 128K |
| Input modalities | text | text, image, file |
| Output modalities | text | text |
| Knowledge cutoff | — | May 31, 2024 |
| Released | Feb 12, 2026 | Aug 7, 2025 |
| Reasoning | always on | always on |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
minimax/minimax-m2.5
Input · 1M tokens
$0.120 + 3%$0.124
Output · 1M tokens
$0.480 + 3%$0.494
FEE — FLAT, EVERY MODEL3%
openai/gpt-5-mini
Input · 1M tokens
$0.250 + 3%$0.258
Output · 1M tokens
$2.00 + 3%$2.06
Cache read · 1M tokens
$0.025 + 3%$0.026
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-m2.5Cheaper
$2.22
$2.16 provider + 3%
openai/gpt-5-mini
$6.70
$6.50 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| MiniMax M2.5 | GPT-5 Mini | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| 3D | 1246 | #31 | 1114 | #73 |
| Code | 1256 | #29 | 1164 | #64 |
| Data viz | 1216 | #40 | 1167 | #62 |
| Game dev | 1242 | #33 | 1194 | #54 |
| SVG | 1208 | #26 | 1149 | #42 |
| UI components | 1228 | #37 | 1161 | #57 |
| Websites | 1265 | #26 | 1167 | #62 |
Head-to-head preference voting. How we filter and rank
Pricing math on a real workload
For 10M input tokens + 2M output tokens: MiniMax M2.5 costs $2.16 on OpenKey (provider price $0.12/M in, $0.48/M out, ×1.03 fee). GPT-5 Mini costs $6.50 for the same workload (provider price $0.25/M in, $2.00/M out, ×1.03 fee). That's a 3x cost gap in M2.5's favor, driven mostly by output pricing — GPT-5 Mini's completion tokens run at $2.06/M on OpenKey versus M2.5's $0.4944/M, a ratio the computed input_price_ratio (0.48) doesn't even capture since output is the bigger driver here. If your app generates a lot of tokens (long code files, long responses), this gap compounds fast. GPT-5 Mini does offer cache reads at $0.025/M provider price, which M2.5 doesn't list — worth checking if you're doing repeated-context calls.
Coding and frontend benchmarks
On Design Arena, M2.5 outranks GPT-5 Mini in every category both models report. Codecategories: M2.5 at elo 1256 (rank 29) vs GPT-5 Mini at elo 1164 (rank 64). Website: M2.5 at elo 1265 (rank 26) vs GPT-5 Mini at elo 1167 (rank 62). Dataviz: 1216 (rank 40) vs 1167 (rank 62). Gamedev: 1242 (rank 33) vs 1194 (rank 54). SVG: 1208 (rank 26) vs 1149 (rank 42). UI components: 1228 (rank 37) vs 1161 (rank 57). 3D: 1246 (rank 31) vs 1114 (rank 73). If your use case is generating or evaluating code, UI, or visual output, M2.5's rank advantage is consistent, not a one-off.
Context and modality differences
GPT-5 Mini's context window is 400,000 tokens against M2.5's 204,800 — roughly double, matching the computed context_ratio of 0.51. GPT-5 Mini also accepts image and file inputs alongside text; M2.5 is text-only in and out. M2.5's max output is larger at 196,608 tokens versus GPT-5 Mini's 128,000, so if you need very long generated responses (not long inputs), M2.5 has more room. Pick based on which end of the pipe is bottlenecked: long documents and images push you to GPT-5 Mini, long generated output pushes you to M2.5.
Reasoning controls and tooling
Both models require reasoning (mandatory: true) — there's no way to disable it on either. GPT-5 Mini exposes four effort levels (high, medium, low, minimal) with medium as default, giving you a lever to trade latency for quality. M2.5 exposes a reasoning_effort parameter too, plus a much longer list of supported parameters overall — including logit_bias, logprobs, top_k, top_logprobs, and min_p, none of which GPT-5 Mini's parameter list includes. If you're doing fine-grained sampling control or logprob-based evaluation, M2.5's parameter surface is wider.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| Code generation / coding agents | MiniMax M2.5 | Higher elo across every Design Arena code and web category (e.g. codecategories 1256 vs 1164) |
| High-volume, cost-sensitive pipelines | MiniMax M2.5 | $2.16 vs $6.50 on a 10M-in/2M-out workload |
| Document or image analysis | GPT-5 Mini | Only GPT-5 Mini accepts image and file inputs; M2.5 is text-only |
| Long-input tasks (large codebases, long docs) | GPT-5 Mini | 400,000 token context vs 204,800 for M2.5 |
| Long-output generation (large files, reports) | MiniMax M2.5 | 196,608 max output tokens vs 128,000 for GPT-5 Mini |
| Fine-grained sampling / logprob analysis | MiniMax M2.5 | Supports logprobs, top_logprobs, top_k, min_p — not in GPT-5 Mini's parameter list |
Questions
- Which is cheaper, MiniMax M2.5 or GPT-5 Mini?
- MiniMax M2.5, by a wide margin. On OpenKey, a 10M input / 2M output workload costs $2.16 with M2.5 versus $6.50 with GPT-5 Mini — roughly 3x. M2.5's provider price is $0.12/M input and $0.48/M output; GPT-5 Mini's is $0.25/M input and $2.00/M output, before the flat 3% fee.
- Does either model support images?
- Only GPT-5 Mini. Its input modalities are text, image, and file. MiniMax M2.5 is text-only on both input and output. If your workload involves screenshots, PDFs, or scanned documents, GPT-5 Mini is the only option of the two.
- Which has the bigger context window?
- GPT-5 Mini, at 400,000 tokens versus 204,800 for MiniMax M2.5 — roughly double, matching the computed context ratio of 0.51. M2.5 counters with a larger max output ceiling of 196,608 tokens against GPT-5 Mini's 128,000.
- Which model scores better on coding benchmarks?
- MiniMax M2.5, on every shared Design Arena category. In codecategories it scores elo 1256 (rank 29) versus GPT-5 Mini's 1164 (rank 64); in website generation, 1265 (rank 26) versus 1167 (rank 62). Both models are available through OpenKey with one key and a flat 3% fee on provider pricing.