AI tool comparison
Gemma 4 vs MiMo-V2.5-Pro
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
AI Models
Gemma 4
Google's sharpest open models — multimodal, 256K context, runs on a Raspberry Pi
75%
Panel ship
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Community
Free
Entry
Gemma 4 is Google DeepMind's fourth-generation open model family, released April 2, 2026, under Apache 2.0. Four variants ship in the family: E2B and E4B edge models that run fully offline on phones, Raspberry Pi, and NVIDIA Jetson; a 26B Mixture-of-Experts model that activates only 3.8B parameters at inference; and a 31B Dense flagship. The 31B scores 1452 on the Arena AI text leaderboard (third among all open models), hits 89.2% on AIME 2026 math, and 85.2% on MMLU Pro — versus Gemma 3's 20.8% on AIME. All four model sizes accept text and image inputs. The edge models additionally handle native audio and video, making them the first on-device models with full multimodal coverage. Context windows reach 256K tokens on the large variants, enabling entire codebases or long documents in a single prompt. Native support for tool use, structured output, and agentic workflows is baked in from the start. For the open-source AI community, Gemma 4 is a watershed: a commercially permissive model that genuinely competes with closed-source alternatives on reasoning benchmarks. Gemma downloads crossed 400 million before this launch — Gemma 4's edge deployment story, combining on-device inference with frontier-class reasoning, looks set to make that number look small.
AI Models
MiMo-V2.5-Pro
Xiaomi's frontier multimodal agent — 1M context, 57% SWE-bench, $1/M tokens
75%
Panel ship
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Community
Paid
Entry
MiMo-V2.5-Pro is Xiaomi's latest and most capable AI model, released April 22, 2026. It combines a 1-million-token context window with multimodal capabilities — vision, audio, and text — in a single agent-ready model. On SWE-bench Pro, it resolves 57.2% of tasks, placing it near the top tier alongside GPT-5.4 and Claude Opus 4.6. What's genuinely surprising isn't the benchmark score — it's the efficiency. MiMo-V2.5-Pro uses roughly 42% fewer tokens than Kimi K2.6 at equivalent benchmark scores, and about 40–60% fewer tokens than comparable frontier models on ClawEval trajectories. That translates directly to lower API costs: the model is priced at approximately $1 per million input tokens. Xiaomi is best known for smartphones and consumer hardware, and MiMo represents a serious pivot into AI services. The company has been quietly building foundation model capabilities for two years, and MiMo-V2.5-Pro is the clearest signal yet that consumer hardware companies won't sit on the sidelines of the foundation model race.
Reviewer scorecard
“Apache 2.0, runs on a Pi, 256K context, beats proprietary models on AIME — this is the open-source AI stack I've been waiting for. The agentic workflow support baked in natively means I'm not bolting on separate tooling. Shipping today.”
“Frontier SWE-bench scores at $1/M tokens is a pricing inflection point. If you're building code agents and paying 3-4x that with other providers, MiMo-V2.5-Pro is worth a serious benchmark on your specific workloads. The 1M context window and multimodal support don't hurt either.”
“The benchmark numbers are impressive on paper, but Gemma 3 was also hyped and underdelivered in production on complex multi-step tasks. The edge models are still unproven outside of Google's own hardware partnerships. Watch the community benchmarks before committing to a migration.”
“Xiaomi has virtually no track record in enterprise AI reliability, SLAs, or developer ecosystems. Their API infrastructure is unproven under production load, and 'matching frontier benchmarks' on SWE-bench doesn't mean it'll perform comparably on your actual use case. Wait for the community to stress-test this in production.”
“On-device frontier-class intelligence with native audio and video is the inflection point for ambient AI. When a $35 Raspberry Pi can run a model that beats last year's GPT-4 on math, the entire economics of edge AI applications change overnight. This is the model that makes AI infrastructure costs asymptotically cheap.”
“This is what happens when smartphone makers with massive scale and tight efficiency cultures enter foundation models. Xiaomi's supply chain discipline maps naturally onto token efficiency. Expect more consumer hardware companies — Samsung, OPPO, others — to ship serious frontier-tier models within the next 12 months.”
“The document and PDF parsing, OCR, chart comprehension, and UI understanding built into every model size is huge for creative workflow automation. I can finally build tools that read design briefs, invoices, and mockups without needing a cloud API call. The offline capability means client data never leaves my machine.”
“Multimodal at $1/M tokens opens up use cases that were just too expensive before. Vision-capable agents at this price point mean small studios and solo creators can build real production workflows around AI vision without the cost anxiety of frontier model pricing.”
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