DeepSeek V3 vs Qwen 3 235B-A22B — flagship MoE showdown
DeepSeek V3 for reasoning + coding heritage. Qwen 3 235B-A22B for instruction-following + faster wall-clock. Both need 192 GB unified memory or multi-GPU SXM.
DeepSeek V3 (671B MoE)
671B params · DeepSeek License · deepseek
Qwen 3 235B-A22B
235B params · Apache 2.0 · qwen
Who wins each use case
Each row is the dimension-weighted verdict for that use case. Use case weights live in src/lib/model-battle/comparator.ts and are public.
What hardware actually runs each model
VRAM math against the canonical hardware ladder. The largest context window that fits with headroom at Q4_K_M appears in each cell.
| Hardware tier | DeepSeek V3 (671B MoE) | Qwen 3 235B-A22B |
|---|---|---|
RTX 3090 (24 GB) Used $700-1,000 — the local-AI workhorse | OOM | OOM |
RTX 4090 (24 GB) Used $1,400-1,900 — current consumer flagship | OOM | OOM |
RTX 5090 (32 GB) Retail $2,000-2,500 — Blackwell consumer | OOM | OOM |
Mac M4 Max (64 GB unified) $4,000-5,000 — Apple Silicon flagship | OOM | OOM |
Dual RTX 3090 (48 GB pooled) ~$1,500-2,000 — workstation budget build | OOM | OOM |
H100 (80 GB) $25K+ — datacenter / cloud rental tier | OOM | OOM |
On RTX 4090 @ Q4_K_M — bandwidth-derived estimate
Computed from each option's sustained TDP × predicted tok/s at $0.16/kWh. Cloud baseline: Claude Sonnet 4.6 (input + output).
Electricity-only cost — excludes the upfront hardware purchase, cooling, and amortized component depreciation. Hardware ROI math lives at /cost-vs-cloud; this line is for "is the marginal token cheaper than Claude?" not "should I buy this rig instead of paying Anthropic." MODELED ESTIMATE.
Both are flagship open-weight Mixture-of-Experts models targeting frontier capability. DeepSeek V3 is larger overall (671B params total, 37B active) with strong reasoning + coding heritage. Qwen 3 235B-A22B is more compact (235B total, 22B active) with sharper instruction-following.
Realistic local deployment for either is Mac Studio M-Ultra-class (192 GB+ unified memory) or multi-GPU NVLink/SXM rigs. Both ship under permissive licenses. The pick is workload + budget.
The verdict for reasoning workloadsPick → Qwen 3 235B-A22B
decisive edge for Qwen 3 235B-A22B — wins 5 of 10 dimensions (1 loss, 4 ties). Verdict reasoning below — no percentage shown on purpose (why).
Qwen 3 235B-A22B is the better fit for reasoning on the dimensions we score, taking 5 of 10 rows. The weighted score (5% vs 40%) reflects use-case priorities: reasoning (40%) outweighs everything else. Both models are worth running — this just tells you which one to reach for first.
| Dimension | DeepSeek V3 (671B MoE) | Qwen 3 235B-A22B | Edge |
|---|---|---|---|
Editorial rating (1-10) Editor rating — single human assessment across reasoning, fluency, tool-use, instruction-following. | 9.0 | unrated | tie |
Parameters (B) | 671.0B | 235.0B | DeepSeek |
Context length (tokens) | 66K | 131K | Qwen |
License (commercial OK?) | ✓ DeepSeek License | ✓ Apache 2.0 | tie |
Decode tok/s on NVIDIA GeForce RTX 4090 (Q4_K_M) Bandwidth-derived estimate. Smaller models stream faster on the same hardware. | 1.4 tok/s | 3.9 tok/s | Qwen |
Fits comfortably on NVIDIA GeForce RTX 4090? | ✕ 543.2 GB short | ✕ 174.6 GB short | Qwen |
Cost to run (local, Q4) Smaller model → less VRAM + less electricity per token. Cross-reference with /cost-vs-cloud for $-anchored math. | 405.1 GB at Q4_K_M | 141.9 GB at Q4_K_M | Qwen |
Community popularity Editorial popularity score — proxy for runtime support breadth + community recipe availability. | 88 | 96 | tie |
Multimodal support | text only | text only | tie |
Released | 2024-12-26 | 2025-04-29 | Qwen |
Which model wins on which VRAM tier. Picks update based on which one fits comfortably + which one’s strengths are unlocked by the available headroom.
| VRAM tier | Pick | Why |
|---|---|---|
| Under 128 GB | → Qwen 3 235B-A22B | Qwen's smaller footprint makes it the only realistic local option in this tier. V3 needs offload that tanks throughput. |
| 192 GB Mac Studio M3 Ultra | → Qwen 3 235B-A22B | Qwen 3 235B fits comfortably at Q4 with headroom; V3 tight. |
| Multi-H100 SXM (640 GB+) | → DeepSeek V3 (671B MoE) | Now V3's full capability is unlocked. Pick it for the reasoning + coding edge. |
DeepSeek V3 or Qwen 3 235B-A22B for high-end local AI?
DeepSeek V3 for reasoning + coding-heavy workloads (its R1 lineage shows). Qwen 3 235B-A22B for instruction-following + agentic loops where the smaller active-parameter count translates to faster wall-clock. Both fit on a 192 GB Mac Studio M-Ultra at heavy quant; neither is a single-card consumer rig.
What hardware can actually run these?
DeepSeek V3 needs roughly 350-400 GB for FP8 weights; Qwen 3 235B-A22B needs roughly 140 GB. At Q4 both shrink: V3 to ~170 GB, Qwen 3 235B-A22B to ~90 GB. Realistic deployments: Mac Studio M3 Ultra 192 GB unified (Qwen 3 235B comfortably, V3 tight), multi-A100/H100 SXM nodes, or rented cloud.
Are these worth running locally vs the cloud API?
For privacy-sensitive workloads or sustained-load deployments where API cost compounds, yes. For occasional use, the API is cheaper. Run /cost-vs-cloud math with your actual monthly token volume before committing to the hardware.
Comparison data computed from live catalog rows + the model-battle comparator (src/lib/model-battle/comparator.ts). For arbitrary pairings outside this curated list, use /model-battle to pick any two models + your hardware.