mimo-v2-flash-comparison

Purpose

Detailed comparison of MiMo V2 Flash against Kimi K2.5, Qwen3-Coder-Next, and GLM-5, covering architecture, benchmarks, pricing, and whether each model can run on a Mac Studio Ultra with 256GB unified memory.

Model Architectures at a Glance

Model	Total Params	Active Params	Context	Architecture	Released
MiMo V2 Flash	309B	~15B	256K	MoE + hybrid SWA/GA	Dec 2025
Kimi K2.5	1T	32B	256K	MoE (384 experts, 8 active)	Jan 2026
Qwen3-Coder-Next	80B	~3B	256K–1M	Ultra-sparse MoE	Feb 2026
GLM-5	744–745B	40–44B	200K in / 128K out	MoE (256 experts, top-8)	Feb 11, 2026
GLM-4.7-Flash	30B	~3B	128K	MoE	Jan 2026

MiMo V2 Flash — Key Architecture Details

309B total / 15B active — smallest active footprint in this class
Hybrid attention: 39 SWA layers + 9 GA layers interleaved at 5:1 ratio; 128-token sliding window
KV-cache reduction: ~6x reduction vs standard attention
Multi-Token Prediction (MTP): Lightweight FFNs embedded in architecture; acts as speculative decoding draft model — 2.6x decoding speedup, 3.6 average acceptance length
Pre-trained on 27T tokens, 32K → 256K context extension
MOPD training (Multi-Teacher On-Policy Distillation): 100K+ verifiable GitHub issues in RL curriculum
License: MIT (fully open-weight)

GLM-5 — Key Architecture Details

744B total / 40–44B active per token
DeepSeek Sparse Attention (DSA) for efficient 200K context
Trained on Huawei Ascend chips with MindSpore — zero NVIDIA dependency
License: MIT (fully open-weight)
IPO: Zhipu AI (Z.ai) listed on Hong Kong Stock Exchange Jan 8, 2026

Benchmark Comparisons

SWE-Bench Verified (Real-World Software Engineering)

Model	SWE-Bench Verified
MiMo V2 Flash	73.4% 🥇
GLM-5	77.8% (#1 overall open-source)
Kimi K2.5	71.3%
Qwen3-Coder-Next	70.6%
GLM-4.7-Flash	59.2%

Note: GLM-5 actually leads at 77.8%, making it the top open-source SWE-Bench performer. MiMo V2 Flash leads among smaller/efficient models at 73.4%.

SWE-Bench Multilingual

Model	Score
MiMo V2 Flash	71.7%
Qwen3-Coder-Next	62.8%
GLM-4.7-Flash	N/A

Mathematical Reasoning (AIME 2025/2026)

Model	Score
GLM-5	92.7% (AIME 2026)
Kimi K2.5	94.5% (AIME 2025)
MiMo V2 Flash	94.1% (AIME 2025)
GLM-4.7-Flash	91.6%

Additional Benchmarks

Benchmark	GLM-5	MiMo V2 Flash
GPQA Diamond	86.0%	—
BrowseComp	75.9% (#1 open-source)	—
Humanity’s Last Exam	50.4%	—

API Pricing Comparison

Model	Input ($/1M tokens)	Output ($/1M tokens)
MiMo V2 Flash	$0.10	$0.30
GLM-5	$1.00	$3.20
GLM-4.7-Flash	~$0.11	~$0.28
Kimi K2.5	$0.15	$2.50
Qwen3-Coder-Next	0.60	2.20

MiMo V2 Flash is the most cost-efficient API option at $0.10/M input.

Running Locally on Mac Studio Ultra 256GB

The Hardware Reality

The Mac Studio with 256GB unified memory is the M3 Ultra (2025). There is no M4 Ultra — Apple skipped it (the M4 Max chip lacks the UltraFusion connector). The next Ultra Mac Studio will be M5 Ultra, expected mid-to-late 2026.

Mac Studio Config	Max RAM	Notes
M4 Max	Up to 128GB	Not Ultra
M3 Ultra	Up to 512GB	Current max; 256GB is a configurable option
M5 Ultra (upcoming)	TBD	Expected mid-late 2026

Can Each Model Run on Mac Studio Ultra 256GB?

MiMo V2 Flash (309B total, ~15B active)

Primary inference tools (SGLang, vLLM) are CUDA-first — no official Apple Silicon support
GGUF quantization via llama.cpp is the viable path
Full FP16: ~620GB → does not fit
INT4/Q4 GGUF: ~155–185GB → fits in 256GB ✅
Verdict: Technically runnable via llama.cpp GGUF quants; expect slow inference (CPU path, no optimized Metal kernel for MoE). Community GGUF versions available.

Kimi K2.5 (1T params)

INT4 quantized: ~500GB minimum → does not fit in 256GB ❌
Need 2× Mac Studio M3 Ultra (512GB total) clustered for reasonable performance
MLX support exists but very slow on single 256GB system
Verdict: Not practical on 256GB single machine.

Qwen3-Coder-Next (80B total, ~3B active)

Q4 GGUF: ~40–50GB → easily fits ✅✅
Excellent Mac performance via MLX or llama.cpp Metal backend
80B model is very manageable; 3B active params means fast inference
Verdict: Best local Mac option — comfortable on 256GB, even on 64GB.

GLM-5 (744B total, 40–44B active)

Full FP16: ~1.65TB → does not fit
8-bit: ~805GB → does not fit
2-bit dynamic GGUF (UD-IQ2_XXS): ~241GB → fits in 256GB ✅ (barely)
1-bit dynamic: ~176GB → fits with headroom
llama.cpp with Metal backend is the path; expect slow inference
Verdict: Marginally runnable at 2-bit or 1-bit quantization. Not fast, but possible.

GLM-4.7-Flash (30B total, ~3B active)

Q4 GGUF: ~17–20GB → trivially fits ✅✅
Excellent Metal/MLX performance (60–80 t/s on M-series)
Verdict: Best “just works” local option at this capability level.

Local Runability Summary

Model	256GB Mac Studio	Quality at 256GB	Recommended?
Qwen3-Coder-Next	✅ Easily	Near-full	✅ Best local
GLM-4.7-Flash	✅ Easily	Full	✅ Fastest local
MiMo V2 Flash	⚠️ Possible (quant)	Degraded	API preferred
GLM-5	⚠️ Barely (1–2bit)	Heavily quantized	API preferred
Kimi K2.5	❌ Too large	N/A	API only

When to Use Each Model

MiMo V2 Flash

Top choice for cost-efficient API usage ($0.10/M input)
Best among efficient models for agentic coding workflows (100K GitHub issue training)
Strong multilingual SWE-Bench performance
Use via API; local deployment not recommended unless you have NVIDIA GPU cluster

Kimi K2.5

Best for sustained agentic tasks (200–300 tool invocations without degrading)
Real-world reported success rate of 93%
Use via API only on Mac; too large for 256GB local

Qwen3-Coder-Next

Best model to run locally on Mac at any memory level
Strong on multilingual code (SWE-Bench Multilingual 62.8%)
Excellent security-focused code generation (SecCodeBench 61.2%)
MLX and llama.cpp both work well

GLM-5

Best overall open-source SWE-Bench score (77.8%)
Best for browser-based/agentic tasks (BrowseComp #1 open-source at 75.9%)
Frontier-level reasoning (GPQA 86.0%, HLE 50.4%)
Via API: $1.00/M input — pricier than MiMo but much cheaper than Claude Opus 4.6
Local: barely possible at 1–2 bit quantization on 256GB

GLM-4.7-Flash

Fastest local deployment on Mac (60–80 t/s)
Best for general reasoning + coding in a small package (30B)
If you need something snappy on a Mac, this is the pick