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Gemini 2.5 Flash vs MiniMax M2.5

GoogleMiniMaxboth via one key, provider price + 3%

Gemini 2.5 Flash and MiniMax M2.5 sit at opposite ends of the design trade-off: one is a multimodal workhorse with a huge context window, the other is a text-only coding specialist with mandatory reasoning and lower prices. Both are available on OpenKey with one API key and a flat 3% fee on top of provider list price. The gap that matters most for most buyers is cost per workload and coding benchmark rank — both of which have concrete numbers below.

Spec vs spec

SpecGemini 2.5 FlashMiniMax M2.5
Context window1.0M205K
Max output66K197K
Input modalitiesfile, image, text, audio, videotext
Output modalitiestexttext
Knowledge cutoffJan 31, 2025
ReleasedJun 17, 2025Feb 12, 2026
Reasoningoptionalalways on

Pricing

Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.

openkey.ai

google/gemini-2.5-flash

Input · 1M tokens

$0.300 + 3%$0.309

Output · 1M tokens

$2.50 + 3%$2.58

Cache read · 1M tokens

$0.030 + 3%$0.031

Cache write · 1M tokens

$0.083 + 3%$0.086

FEE — FLAT, EVERY MODEL3%

openkey.ai

minimax/minimax-m2.5

Input · 1M tokens

$0.120 + 3%$0.124

Output · 1M tokens

$0.480 + 3%$0.494

FEE — FLAT, EVERY MODEL3%

One workload, priced on both

10M input + 2M output tokens at each model's price, flat 3% fee included.

google/gemini-2.5-flash

$8.24

$8.00 provider + 3%

minimax/minimax-m2.5Cheaper

$2.22

$2.16 provider + 3%

Benchmarks

Design Arena categories where both models have results. Higher Elo and lower rank win.

Gemini 2.5 FlashMiniMax M2.5
CategoryEloRankEloRank
3D1148#641246#31
Code1153#691256#29
Data viz1171#581216#40
Game dev1131#721242#33
SVG1078#581208#26
UI components1148#631228#37
Websites1158#681265#26

Head-to-head preference voting. How we filter and rank

Pricing math: same workload, different bill

OpenKey price is provider price × 1.03. Gemini 2.5 Flash: $0.30/M input → $0.309/M, $2.50/M output → $2.575/M. MiniMax M2.5: $0.12/M input → $0.1236/M, $0.48/M output → $0.4944/M.

Run 10M input tokens + 2M output tokens on each: - Gemini 2.5 Flash: **$8.00** - MiniMax M2.5: **$2.16**

That's a 3.7x cost difference for identical volume. The input price ratio alone is 2.5x (Gemini's $0.30 vs MiniMax's $0.12 per million). If you're running high-volume batch jobs — scraping, summarization, bulk classification — MiniMax M2.5's price floor matters more than any benchmark gap.

Coding and design benchmarks

Design Arena scores MiniMax M2.5 ahead of Gemini 2.5 Flash across every category tracked. In codecategories, MiniMax M2.5 ranks 29 (elo 1256, 56.8% win rate) versus Gemini 2.5 Flash's rank 69 (elo 1153, 46.9% win rate). Website generation: MiniMax M2.5 ranks 26 (elo 1265, 57.5% win) versus Gemini's rank 68 (elo 1158, 47.1% win). SVG: MiniMax ranks 26 (elo 1208, 54.5% win) versus Gemini's rank 58 (elo 1078, 43.1% win). Gamedev: MiniMax ranks 33 (elo 1242, 55.5% win) versus Gemini's rank 72 (elo 1131, 44.3% win). Every category shows the same pattern — MiniMax M2.5 sits 30-40 ranks higher.

Context and modality

Gemini 2.5 Flash has a 1,048,576-token context window versus MiniMax M2.5's 204,800 tokens — a 5.12x ratio. If you're feeding entire codebases, long transcripts, or multi-document RAG contexts in one call, Gemini's window gives you room MiniMax doesn't have. Gemini also accepts image, audio, video, and file inputs alongside text; MiniMax M2.5 is text-only in and out. On output, MiniMax allows up to 196,608 completion tokens versus Gemini's 65,535 — useful if you need MiniMax to generate very long single responses, like full codebases or long-form docs, in one pass.

Reasoning and tool support

MiniMax M2.5 has mandatory reasoning — it always runs a reasoning pass, and supports `reasoning_effort` as a tunable parameter alongside `top_k`, `min_p`, `logprobs`, and `parallel_tool_calls`. Gemini 2.5 Flash treats reasoning as optional (`include_reasoning` is supported but not forced) and has a shorter parameter list — no `top_k`, no `logit_bias`, no `parallel_tool_calls`. If your pipeline depends on fine-grained sampling control or explicit parallel tool calls, MiniMax's parameter surface is wider. Gemini's knowledge cutoff is documented at 2025-01-31; MiniMax M2.5's cutoff isn't published, so if training recency matters for your use case, Gemini is the one you can verify.

Which model for which job

Use casePickWhy
High-volume batch text processingMiniMax M2.5$2.16 vs $8.00 for a 10M-in/2M-out workload
Coding agents / UI generationMiniMax M2.5Ranks 26-40 across Design Arena coding categories vs Gemini's 58-72
Long-document or multi-file contextGemini 2.5 Flash1,048,576-token context is 5.12x MiniMax's 204,800
Multimodal input (image, audio, video)Gemini 2.5 FlashOnly model of the two that accepts non-text inputs
Very long single-response generationMiniMax M2.5196,608 max output tokens vs Gemini's 65,535
Pipelines needing a documented knowledge cutoffGemini 2.5 FlashCutoff is listed as 2025-01-31; MiniMax's cutoff isn't published

Questions

Which is cheaper, Gemini 2.5 Flash or MiniMax M2.5?
MiniMax M2.5, by a wide margin. On OpenKey (provider price + 3%), a 10M-input/2M-output workload costs $2.16 on MiniMax M2.5 versus $8.00 on Gemini 2.5 Flash — roughly 3.7x cheaper. Input tokens alone are 2.5x cheaper on MiniMax ($0.1236/M vs $0.309/M).
Which model is better for coding?
MiniMax M2.5 scores higher across every Design Arena coding-adjacent category. In codecategories it ranks 29 (elo 1256) versus Gemini 2.5 Flash's rank 69 (elo 1153). In website generation MiniMax ranks 26 versus Gemini's 68. The gap holds in svg, gamedev, and uicomponent categories too.
Does either model handle images or audio?
Only Gemini 2.5 Flash does. It accepts text, image, audio, video, and file inputs (output is text-only). MiniMax M2.5 is text-in, text-out only, so it's not an option if your pipeline needs to process images or audio.
Which has the larger context window?
Gemini 2.5 Flash, at 1,048,576 tokens versus MiniMax M2.5's 204,800 tokens — a 5.12x difference. MiniMax compensates with a larger max output of 196,608 tokens versus Gemini's 65,535, so it's better suited to generating long single responses rather than ingesting huge inputs.

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