DeepSeek V3 vs Qwen 3.5 on Mac: Speed, RAM and Winner (2026)

TL;DR: DeepSeek-V3 (671B total, 37B active) posts 88.5 on MMLU and 82.6 on HumanEval-Mul — but its size limits it to 96GB+ Mac Studios. Qwen3.5-35B-A3B scores 85.3 MMLU-Pro, fits a 24GB MacBook Pro, and generates faster. For most Mac users, Qwen 3.5 is the practical winner.

Update (April 2026): DeepSeek V4 (1T params) is expected imminently. V3 remains the latest released DeepSeek model. Qwen 3.5 is still Alibaba's current flagship. We will publish a V4 comparison once weights drop — see DeepSeek V4: What Mac Users Need to Know.

The two giants of open-source local AI are here. DeepSeek-V3 and Qwen 3.5 both promise frontier-level quality on consumer hardware. But which one actually delivers the best experience on your Mac?

Every benchmark below comes from the official model cards. Speed and RAM figures are modelfit.io estimates based on quantized model sizes and Apple Silicon memory bandwidth — they are marked as such.

Who Are the Contenders?

Model	Architecture	Total Params	Active Params	Context	MoE?
DeepSeek-V3	MLA + MoE	671B	37B	128K	✅ Yes
Qwen 3.5-122B	Transformer + MoE	122B	10B	262K (ext. 1M)	✅ Yes

DeepSeek-V3 specs: DeepSeek-V3 model card. Qwen specs: Qwen3.5-122B-A10B model card.

Both use Mixture of Experts (MoE) — only activating a subset of parameters per token. This makes massive models runnable on consumer hardware.

Round 1: Which Model Has Higher Quality?

Each lab reports different benchmark suites, so here are the headline knowledge scores from each model card.

Model	Benchmark	Score	Source
DeepSeek-V3	MMLU (EM)	88.5	DeepSeek-V3 card
Qwen 3.5-122B-A10B	MMLU-Pro	86.7	Qwen3.5-122B card
Qwen 3.5-35B-A3B	MMLU-Pro	85.3	Qwen3.5-35B card

Winner: DeepSeek-V3, narrowly — with a caveat. MMLU and MMLU-Pro are different tests, so this is not an apples-to-apples row. What the cards support: DeepSeek-V3 is an exceptional generalist, and the much smaller Qwen 3.5 models land in the same quality neighborhood while activating a fraction of the parameters.

DeepSeek-V3 is particularly strong in:

Mathematical reasoning
Code generation
Complex multi-step tasks

Round 2: Which Is Faster on Apple Silicon? (Estimated)

These tok/s figures are modelfit.io estimates derived from model size and memory bandwidth, not lab benchmarks. Actual speeds vary by quantization and system load.

On Mac Studio M2 Ultra (128GB) — Estimated

Model	Est. Speed	Notes
Qwen 3.5-122B	~35 tok/s	Only 10B active params per token
DeepSeek-V3 (Q3)	~12 tok/s	Slower but usable

Winner: Qwen 3.5-122B (roughly 3× faster) — fewer active parameters means less memory traffic per token.

On MacBook Pro M4 Max (36GB) — Estimated

DeepSeek-V3 can't run here (needs roughly 72GB+ for Q4, 56GB+ for Q3).

Model	Est. Speed	Notes
Qwen 3.5-35B-A3B	~42 tok/s	Excellent
DeepSeek-V2.5 (smaller)	~28 tok/s	Alternative

Winner: Qwen 3.5-35B-A3B — DeepSeek-V3 simply doesn't fit.

Round 3: How Much RAM Do They Need?

RAM figures are estimates based on quantized file sizes plus context overhead.

Model	Min RAM (Q4)	Recommended	VRAM Pressure
DeepSeek-V3	~72GB	96GB+	🔴 High
Qwen 3.5-122B	~72GB	96GB+	🔴 High
Qwen 3.5-35B-A3B	~20GB	24GB	🟢 Low

DeepSeek-V3's 671B parameters make it incredibly VRAM-hungry. Even Q3 quantization needs ~56GB.

Winner: Qwen 3.5 (more flexible sizing)

Round 4: Which Has the Longer Context Window?

Model	Context	Source
DeepSeek-V3	128K	DeepSeek-V3 card
Qwen 3.5-122B / 35B-A3B	262K native, extensible to 1M	Qwen3.5 cards
Qwen 3.5-Flash (hosted API)	1M by default	Qwen3.5-35B card

Winner: Qwen 3.5 (double the native context)

Longer context means:

Larger codebases in one prompt
Longer document analysis
Better multi-turn conversations

Round 5: Which Codes Better?

Again, the labs report different coding suites, so each score comes from its own model card.

Model	Benchmark	Score	Source
DeepSeek-V3	HumanEval-Mul (Pass@1)	82.6	DeepSeek-V3 card
Qwen 3.5-122B-A10B	SWE-bench Verified	72.0	Qwen3.5-122B card
Qwen 3.5-35B-A3B	SWE-bench Verified	69.2	Qwen3.5-35B card
Qwen 3.5-35B-A3B	LiveCodeBench v6	74.6	Qwen3.5-35B card

The picture: DeepSeek-V3 is a strong code generator on classic completion tests. Qwen 3.5's SWE-bench Verified scores measure something harder — fixing real GitHub issues agentically — and a 35B model clearing 69 there while fitting in 20GB is remarkable.

Round 6: Which Should You Actually Use?

When to Choose DeepSeek-V3

✅ Choose DeepSeek-V3 if:

You have Mac Studio 128GB
Maximum quality is priority
Heavy coding workloads
You're okay with slower generation (~12 tok/s est.)

When to Choose Qwen 3.5

✅ Choose Qwen 3.5 if:

You have MacBook Pro 24-36GB (35B-A3B model)
Speed matters (~35-45 tok/s est.)
Long context needed (262K+)
You want more model size options

The Verdict by Use Case

For MacBook Pro Users (24-36GB RAM)

Winner: Qwen 3.5-35B-A3B

DeepSeek-V3 simply won't fit. Qwen 3.5-35B-A3B delivers:

85.3 MMLU-Pro (model card)
~42 tok/s estimated on M4 Max
~20GB RAM usage (fits comfortably)

For Mac Studio Users (64GB+ RAM)

Winner: Depends on priority

Priority	Winner	Model
Quality	DeepSeek-V3	671B, 88.5 MMLU
Speed	Qwen 3.5	122B, ~35 tok/s est.
Balance	Qwen 3.5	35B-A3B on 24GB

Our Recommendation

For most users with MacBook Pro 24-48GB:

→ Qwen 3.5-35B-A3B is the practical winner.

For power users with Mac Studio 128GB who want maximum quality:

→ DeepSeek-V3 is worth the VRAM investment.

Quick Reference

# Qwen 3.5-35B-A3B (recommended for most)
ollama run qwen3.5:35b-a3b

# DeepSeek-V3 (Mac Studio only)
ollama run deepseek-v3

# Qwen 3.5-122B (Mac Studio alternative)ollama run qwen3.5:122b-a10b

Related: Learn about the Qwen 3.5 Medium series in detail, see our MacBook Pro recommendations, or check the full local vs cloud benchmark.

Frequently Asked Questions

Can I run DeepSeek-V3 on a MacBook Pro?

No. DeepSeek-V3 requires roughly 72GB RAM for Q4 quantization (about 56GB for Q3). Only a Mac Studio with 96GB+ RAM can run it comfortably. For MacBook Pro users, Qwen 3.5-35B-A3B is the recommended alternative.

Which model is better for coding on Mac?

DeepSeek-V3 reports 82.6 on HumanEval-Mul; Qwen 3.5-122B reports 72.0 on the harder SWE-bench Verified agentic test (model cards). If you have the RAM, DeepSeek-V3 is a top-tier code generator. On a MacBook Pro with 24-36GB, Qwen 3.5-35B-A3B (69.2 SWE-bench Verified) is the best available coding model.

How fast is Qwen 3.5 compared to DeepSeek-V3 on Apple Silicon?

By our estimates, Qwen 3.5-122B runs around 35 tokens per second on Mac Studio M2 Ultra — roughly 3x faster than DeepSeek-V3 at ~12 tok/s. The smaller Qwen 3.5-35B-A3B reaches an estimated 42 tok/s on MacBook Pro M4 Max. Active parameter count drives the difference: 10B and 3B versus 37B.

Should I use Q3 or Q4 quantization for DeepSeek-V3?

Q4_K_M gives better quality but the full file is too large for any Mac. Q3_K_M reduces the footprint enough to fit a 128GB Mac Studio with heavy memory pressure. Quality loss from Q3 is noticeable but acceptable for most tasks.

What is the best Mac configuration for running both models?

A Mac Studio with 128GB unified memory can run both DeepSeek-V3 (Q3) and Qwen 3.5-122B (Q4). For the best experience with DeepSeek-V3, 192GB is ideal. Use modelfit.io to get personalized recommendations.

---

Related Model Families:

DeepSeek Models — R1 and V3 reasoning models for local AI
Qwen Models — Full Qwen lineup from 0.5B to 235B

Sources: DeepSeek-V3 model card, Qwen3.5-35B-A3B model card, Qwen3.5-122B-A10B model card. Benchmarks verified against the raw model cards, June 2026. Speed and RAM figures are modelfit.io estimates and may vary with quantization and hardware.