DeepSeek V4 Flash vs MiMo-V2.5 vs Hy3: The Real Open-Weight Cost Battle of 2026

Three Chinese labs, three genuinely different architectural bets, and a price gap wide enough to matter: one model runs at roughly a quarter of a cent per thousand tokens, another packs 295 billion parameters into a footprint that fits on a single server, and a third scores nearly seventeen points higher on hard agentic evaluations while activating fewer than half the parameters of its rival.

None of that tells you which one to actually deploy. This post does.

Introduction

DeepSeek V4 Flash, MiMo-V2.5, and Hunyuan Hy3 are three of the sharpest open-weight releases to come out of China's frontier labs in 2026, and they're frequently confused with each other because all three chase the same goal: flagship-adjacent capability at a fraction of flagship cost. But the paths they take to get there are structurally different, and picking between them without understanding those differences means optimizing for the wrong thing.

This post breaks down what each model actually is, the benchmark data from independent evaluators, and a concrete decision framework for which one fits which production workload.

At a Glance: The Full Comparison

Two numbers in that table do more work than the rest: DeepSeek's roughly 4.3x price advantage over MiMo-V2.5-Pro, and Hy3's sub-300GB footprint against comparable open models that need an 8-GPU cluster. Everything else in the comparison is a matter of degree — those two are structural.

What Each Model Actually Is

DeepSeek V4 Flash, released April 2026 as the efficient tier of the V4 generation, ships as a 284-billion-parameter Mixture-of-Experts model with only 13 billion parameters active per forward pass — the smallest active-parameter footprint of the three by a wide margin. It sits alongside the much larger DeepSeek V4 Pro (1.6 trillion total parameters, 49 billion active) as the deliberately lean, cost-optimized option in DeepSeek's lineup. DeepSeek's own technical report notes that despite a substantially smaller parameter budget, V4 Flash's base model outperforms the larger V3.2 generation across a wide range of benchmarks — evidence that architectural refinement and training data quality, not just raw scale, drove this generation's gains.

MiMo-V2.5, from Xiaomi, comes in standard and Pro variants and is a meaningfully larger model — roughly 260% more parameters than DeepSeek V4 Flash by one independent comparison. Xiaomi's own technical documentation reports MiMo achieving performance comparable to Kimi-K2-Thinking and DeepSeek-V3.2-Thinking across most reasoning benchmarks, positioning it as a reasoning-focused competitor rather than a pure efficiency play.

Hunyuan Hy3, from Tencent, released its full version on July 6, 2026, following an April preview. It's a 295-billion-parameter MoE model activating just 21 billion parameters per token — including a distinct 3.8-billion-parameter MTP (Multi-Token Prediction) layer for faster inference. Critically, Hy3 ships under Apache 2.0 with no field-of-use or geographic restrictions, and Tencent has explicitly positioned it around production deployment rather than benchmark leadership alone — the model reportedly runs across more than 50 Tencent products internally, with the company citing a double-digit percentage-point improvement in task resolution for one internal deployment.

The Benchmark Data

DeepSeek V4 Flash vs MiMo-V2.5: a near-even split, decided by price

Independent comparison data from LLM-Stats shows a genuinely mixed picture: DeepSeek V4 Flash-Max leads on four benchmarks (GPQA, Humanity's Last Exam, MMLU-Pro, SWE-Bench Verified), while MiMo-V2.5-Pro leads on three (GDPval-AA, SWE-Bench Pro, Terminal-Bench 2.0) — a slight overall edge to DeepSeek on raw capability, but not a decisive one.

Artificial Analysis's own head-to-head tells a related but distinct story depending on configuration: in reasoning mode at maximum effort, DeepSeek V4 Flash scores 50 on the Intelligence Index against MiMo-V2.5's 37 — a real gap. But BenchLM's aggregated public score estimate puts the two much closer: DeepSeek V4 Flash at 58.02 versus MiMo-V2.5 at 57.91, with the 90% confidence intervals overlapping — meaning that comparison should be read as a lead, not a settled winner.

Head-to-head benchmark scores

Where the decision actually gets easy is pricing. DeepSeek V4 Flash runs roughly 4.3x cheaper per token than MiMo-V2.5-Pro on a blended basis — $0.10/$0.20 per million input/output tokens versus $0.43/$0.87. For workloads where the two models perform comparably, that price gap alone should usually decide the outcome.

Hy3 vs the field: efficiency with a real coding tradeoff

Hy3's positioning is different — it isn't chasing DeepSeek or MiMo directly, it's chasing GLM-5.2, the roughly 744-billion-parameter open model that has held the open-weight coding crown since mid-2026. And the comparison there is genuinely nuanced.

On raw coding benchmarks, GLM-5.2 still wins clearly: 84.2% versus Hy3's 78.0% on SWE-bench Verified, and 46.2% versus 28.0% on DeepSWE, according to Tencent's own published appendix. That's a real, acknowledged gap — Tencent isn't hiding it.

But Hy3 leads the open-weight field specifically on agentic search and tool orchestration, though not necessarily beating closed frontier models on the same tasks.

The number that actually changes deployment decisions: Hy3's FP8 weights fit under 300GB, against GLM-5.2's roughly 744GB — less than half the memory footprint, and the practical difference between fitting on a single serving node versus needing an 8x H200 cluster as the minimum viable deployment. One independent report separately puts Hy3's SWE-bench Verified preview score at 74.4% — achieved with only 21 billion active parameters, a result the same report describes as coding parity with systems roughly five times its active parameter count.

How to Access Each Model

DeepSeek V4 Flash — Available via DeepSeek's own OpenAI-compatible API endpoint, plus routing aggregators like OpenRouter. Defaults to thinking mode on; disable it via the request-body flag "thinking": {"type": "disabled"} for latency-sensitive, non-reasoning tasks — a meaningful cost lever for workloads that don't need a visible reasoning trace.

MiMo-V2.5 — Available through Xiaomi's API and third-party routing platforms including OpenRouter, with both standard and Pro variants exposed separately. Pro carries the pricing and performance premium described above; the standard variant is a lighter option worth benchmarking before defaulting to Pro.

Hy3 — The most deployment-flexible of the three. Available via Tencent Cloud's API, a free OpenRouter route (time-limited promotional access — confirm current status before production use), or fully self-hosted from Hugging Face weights under Apache 2.0. For self-hosting, Tencent has published both FP8 weights (fitting under 300GB, deployable on 8x H20-class GPUs) and 1-bit/4-bit GGUF quantizations for llama.cpp that run on a single 128GB-VRAM rig — removing the multi-GPU cluster requirement entirely for teams willing to accept the quality tradeoff of aggressive quantization. Deployment via SGLang supports the MTP layer directly through --speculative-algorithm EAGLE for faster inference.

Choose DeepSeek V4 Flash when:

• Cost per token is the dominant constraint on your deployment — its roughly 4.3x price advantage over MiMo-V2.5-Pro is difficult to justify not taking if performance is comparable for your workload

• You need the smallest active-parameter footprint among the three — 13B active parameters means lower inference compute per request, which compounds directly into infrastructure savings at volume

• Your workload leans toward knowledge-heavy tasks — GPQA, MMLU-Pro, and Humanity's Last Exam are exactly where V4 Flash shows its clearest advantage over MiMo

Choose MiMo-V2.5 when:

• Reasoning depth matters more than cost efficiency for your specific use case, and your evaluation on your own workload confirms the reasoning-mode gap holds

• You need a larger response ceiling — MiMo-V2.5 supports up to 131,072 output tokens versus V4 Flash's 65,536, relevant for tasks that generate long structured output

• Agentic and terminal-based benchmarks matter specifically — GDPval-AA, SWE-Bench Pro, and Terminal-Bench 2.0 favor MiMo in the head-to-head data

Choose Hy3 when:

• Self-hosting on constrained hardware is a hard requirement — the sub-300GB FP8 footprint fitting a single node, rather than an 8-GPU cluster, is a genuine deployment-cost unlock that neither DeepSeek nor MiMo directly competes on at this parameter scale

• Agentic search and tool orchestration are the core workload — Hy3's open-field leadership on BrowseComp, DeepSearchQA, and MCP-Atlas is specifically relevant for agents that need to search, retrieve, and call tools reliably

• Licensing flexibility matters — Apache 2.0 with no field-of-use or geographic restrictions is about as permissive as open-weight licensing gets, relevant for commercial deployment without legal ambiguity

• You're not primarily optimizing for raw coding benchmark leadership — if SWE-bench-style coding performance is the top priority, GLM-5.2 still wins that specific comparison, and that tradeoff should be made consciously, not by default

Real-World Use Cases

DeepSeek V4 Flash: high-volume, cost-sensitive production workloads

Customer support triage and classification at scale. With input pricing at $0.10 per million tokens and output at $0.20, a support platform processing millions of tickets a month can run classification, routing, and first-draft response generation at a cost structure that would be difficult to justify with a pricier model — especially given V4 Flash's demonstrated strength on knowledge-heavy benchmarks like MMLU-Pro, which map reasonably well onto understanding varied support content.

Internal developer tooling. V4 Flash's SWE-Bench Verified lead over MiMo, combined with its output speed advantage (109.2 tokens/sec versus MiMo's 73.6), makes it a reasonable default for IDE-integrated code assistance and CI-adjacent tooling where response latency directly affects developer flow state.

Bulk content and data pipeline processing. Any workload that fans out across a large volume of similar, moderately complex tasks — document summarization at scale, structured data extraction from varied source formats — benefits directly from the 4.3x cost advantage over MiMo-V2.5-Pro, assuming the accuracy gap (or lack thereof) is validated against your own data first.

MiMo-V2.5: reasoning-heavy and agentic workflows where quality justifies cost

Complex multi-step agent orchestration. MiMo's lead on GDPval-AA and Terminal-Bench 2.0 — both benchmarks specifically designed around agentic, tool-using task completion — makes it a stronger fit for agents that need to plan, execute, and self-correct across many steps, where a reasoning failure partway through a long chain is expensive to recover from.

Long-form structured output generation. The 131,072-token output ceiling, roughly double V4 Flash's limit, matters concretely for tasks like generating full technical specifications, long structured reports, or multi-section legal-adjacent documents in a single pass without needing to chain multiple requests together.

Research and analysis tasks with genuine reasoning depth. Where the reasoning-mode Intelligence Index gap (50 vs 37) actually reflects real task difficulty — synthesizing conflicting information across sources, multi-hop logical inference — the cost premium over V4 Flash is a reasonable trade for fewer downstream errors.

Hy3: self-hosted agentic search and cost-constrained infrastructure

On-premises deployment for regulated or data-sovereignty-sensitive workloads. Because Hy3's FP8 weights fit under 300GB — a single server rather than an 8-GPU cluster — organizations in finance, healthcare, or government contexts that need to keep model inference entirely on their own infrastructure get a meaningfully lower hardware bar to clear than comparable open models like GLM-5.2.

Enterprise search and knowledge-retrieval agents. Hy3's open-field leadership on BrowseComp and DeepSearchQA directly targets the core capability an internal knowledge-search agent needs: finding the right information across a large, messy corpus and synthesizing an accurate answer. Tencent cites its internal WorkBuddy product as a proof point here, reporting an 18-percentage-point jump in task resolution after deploying Hy3 — a figure worth treating as vendor-reported and validating independently rather than as an audited third-party result.

Tool-calling agents integrated with internal systems. The MCP-Atlas tool orchestration lead (79.1) is specifically relevant to agents that need to reliably call internal APIs, databases, and services — the kind of production agent work that breaks in subtle ways when a model hallucinates a parameter or misroutes a tool call.

Edge and resource-constrained deployment. The 1-bit/4-bit GGUF quantizations running on a single 128GB rig open Hy3 to teams that can't justify even a single high-end GPU server — a genuinely different deployment tier than DeepSeek or MiMo target at comparable capability.

A few things worth being direct about, since vendor-reported numbers dominate this space more than in most model comparisons.

Some circulating Hy3 numbers are launch claims, not verified benchmarks. Independent reporting flags that certain SWE-Bench Multilingual and Terminal-Bench figures cited in early social posts about Hy3 didn't appear on Tencent's own Hugging Face model card at publish time. Treat directional claims from launch-day social posts with more skepticism than numbers published in the accompanying technical report or appendix.

Confidence intervals matter more than point estimates in close comparisons. The DeepSeek V4 Flash vs MiMo-V2.5 gap on BenchLM's aggregated score (58.02 vs 57.91) is close enough that the stated 90% confidence intervals overlap — meaning for many real workloads, the practical difference may not be statistically meaningful, and price becomes the actual tiebreaker.

"Matches models 2-5x its size" is Tencent's framing, not an independent audit. It's a defensible claim given the SWE-bench and agentic-search data available, but it's Tencent's own positioning language, repeated across its marketing material — worth validating against your own evaluation harness before treating it as settled fact for your specific workload.

Key Takeaways

• DeepSeek V4 Flash wins on pure cost efficiency — roughly 4.3x cheaper per token than MiMo-V2.5-Pro, with the smallest active-parameter footprint of the three models, at benchmark performance that's competitive or ahead on knowledge-heavy evaluations.

• MiMo-V2.5 has a real edge on agentic and reasoning-heavy benchmarks — GDPval-AA, SWE-Bench Pro, Terminal-Bench 2.0, and a notably larger maximum output length — but it comes at a real cost premium that needs to be justified by your specific workload.

• Hy3's real differentiator isn't a benchmark score — it's deployment economics. A sub-300GB FP8 footprint that fits a single server, versus a competitor requiring an 8-GPU cluster, is the kind of infrastructure difference that changes what's actually viable to self-host.

• Hy3 explicitly trades raw coding benchmark leadership for agentic search strength. It doesn't beat GLM-5.2 on SWE-bench, and Tencent's own published data says so directly — but it leads the open field on tool orchestration and long-context retrieval.

• Close comparisons should be settled by price and your own eval, not vendor benchmark tables alone. Several of the head-to-head numbers between these three models sit within overlapping confidence intervals — a "win" on a benchmark table doesn't always translate to a meaningful difference on your actual traffic.

Conclusion

None of these three models is a universal "best open-weight model" answer, and treating the comparison as a single leaderboard misses what actually makes each one worth choosing. DeepSeek V4 Flash is the cost-efficiency play. MiMo-V2.5 is the reasoning-and-agentic play, at a real price premium. Hy3 is the deployment-economics play — genuinely competitive agentic search and tool-use capability that fits on hardware the other two don't specifically optimize around.

The right choice depends on which constraint actually binds your production system: token cost, reasoning depth, or the size of the server you can realistically deploy on. Answer that question honestly about your own workload before reading another benchmark table.

References

• LLM-Stats — DeepSeek-V4-Flash-Max vs MiMo-V2.5-Pro: Benchmarks, Pricing & Which Is Better in 2026

• Artificial Analysis — DeepSeek V4 Flash (Non-reasoning) vs MiMo-V2.5

• Artificial Analysis — DeepSeek V4 Flash 0731 (Reasoning, Max Effort) vs MiMo-V2.5

• BenchLM — DeepSeek V4 Flash vs MiMo-V2.5: Benchmarks & Cost

• arXiv:2606.19348 — DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

• arXiv:2601.02780 — MiMo-V2-Flash Technical Report

• Digital Applied — Tencent's Hunyuan Hy3: Open-Weight Reasoning Arrives

• AIMadeTools — Tencent Hy3: 74.4% SWE-bench With Only 21B Active Parameters

• Lushbinary — Tencent Hunyuan Hy3 Developer Guide & Benchmarks

• ExplainX — Tencent Hy3: 295B Open MoE for Agentic Coding

• GIGAZINE — Tencent has released its AI model 'Hy3' as an open model

At Kynodex, we build the evaluation and routing infrastructure that turns benchmark tables into production decisions — testing open-weight models like these against real workloads, not vendor marketing. If your team is evaluating open-weight models for self-hosted deployment, talk to us.

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