AI tool comparison
DeepSeek V4-Pro 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.
Foundation Models
DeepSeek V4-Pro
1.6T-param MoE model, 1M context, Nvidia-free — just dropped Apache 2.0
75%
Panel ship
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Community
Paid
Entry
DeepSeek just dropped V4-Pro and V4-Flash simultaneously — and it's a statement release. V4-Pro packs 1.6 trillion total parameters in a MoE architecture with only 49B active per token, a 1-million-token context window, and a hybrid attention system (Compressed Sparse Attention + Heavily Compressed Attention) that requires just 27% of single-token inference FLOPs compared to V3.2. Both models are Apache 2.0. The hardware story is arguably the bigger news: V4 was trained entirely on Huawei Ascend 950PR chips, zero NVIDIA. That's a geopolitical and technical milestone — it validates China's domestic AI compute stack at frontier scale. The Engram Memory System gives V4 conditional context recall (94% at 128K tokens vs ~45% for V3.2), enabling genuinely long-context reasoning. V4-Flash at 284B parameters (13B active) is the cheaper, faster sibling for production use. Pricing is expected around $0.30/M tokens for Pro. The timing — released to HN today with 99+ points within hours — confirms this as an immediate conversation in the developer community about whether open-weight frontier models have finally matched proprietary ones.
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 with 1M context and frontier-level benchmarks changes the commercial calculus entirely. Self-host for sensitive workloads, use the API for production — the 49B active params means reasonable inference costs if you have the hardware.”
“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.”
“Benchmark claims from DeepSeek have historically been hard to independently replicate at launch. The Huawei chip story is compelling but also means the Western open-source deployment story requires significant hardware work. And 1.6T parameters is not consumer hardware territory.”
“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.”
“V4's Nvidia-free training stack is a geopolitical inflection point as much as a technical one. It proves the export control strategy isn't containing China's AI progress — and gives the global open-source community a frontier model with no licensing restrictions.”
“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.”
“A 1M-token context model at $0.30/MTok Apache 2.0 means long-form creative projects — novels, screenplays, brand bibles — can finally be processed holistically. The Flash variant's low cost makes it accessible even for creative side projects with tight budgets.”
“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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