DeepSeek V3.2 vs Llama 4 vs Qwen 3 (2026): Cost-per-Token and Which to Pick

Three open-source model families are production-ready as of May 2026: DeepSeek V3.2 Speciale, Llama 4 Scout and Maverick, and Qwen 3. Each targets different hardware budgets and use cases.

Picking the wrong one costs money. DeepSeek V3.2 Speciale needs 8x H100 GPUs at minimum. Qwen3-32B runs on a single H100. Llama 4 Scout sits between them in quality but has a context window no other model touches. This post breaks down which one to pick for your workload, with benchmark numbers, cost per million tokens, and hardware requirements for each configuration.

Alibaba also sells Qwen through its own API, and the closed tiers there are not the same models you can download. Our Qwen API pricing vs self-hosted LLMs breakdown covers that rate card and what the open checkpoints give up against it, which is the comparison to run before you assume self-hosting is a like-for-like swap.

TL;DR: DeepSeek V3.2 vs Llama 4 vs Qwen 3, Which to Pick

Qwen3-32B for coding, Llama 4 Scout for chat and 10M-token RAG, DeepSeek V3.2 Speciale only for math and reasoning. Spheron rates live as of 12 Nov 2026.

Single-GPU picks fit on one Spheron H100 SXM5.

Pricing fluctuates based on GPU availability. Spheron rates above are live as of 12 Nov 2026; other providers reflect their most recent published rates and may have changed. Check current GPU pricing → for live rates.

Model Overview

† Optimized FP8 quantization compresses the weight footprint to approximately 640 GB (below the naive 685 GB you would expect at 1 byte per parameter), which just fits within the 640 GB aggregate VRAM of 8x H100 SXM5 80GB. At high KV cache usage or large batch sizes, you may still need expert offloading strategies. See the DeepSeek V3.2 Speciale deployment guide for details.

¶ Llama 4 Maverick INT4 size: the ~200 GB figure is the theoretical minimum at 4 bits per parameter (400B × 0.5 bytes). Published GGUF Q4 quantizations on Hugging Face are larger (~243 GB) due to metadata and block-level quantization overhead. 4x H100 80GB (320 GB aggregate) fits the ~200 GB AWQ/GPTQ INT4 size but is tight for GGUF Q4. If using GGUF Q4, plan for careful memory management or use 4x A100 80GB as a fallback. For the full FP16/INT8/INT4 breakdown on both Llama 4 models, including what Scout's 10M context window actually costs in KV cache, see our Llama 4 VRAM requirements guide.

The active parameter count is what drives inference cost on a per-token basis. Both Llama 4 variants activate just 17B parameters per forward pass despite having 109B or 400B total weights in memory. DeepSeek V3.2 Speciale activates 37B. Qwen3-32B is a dense model, so all 32.8B parameters are active on every token.

The critical distinction for hardware planning: all model weights must reside in VRAM regardless of how many parameters are active. See our GPU memory requirements guide and the 2026 GPU requirements cheat sheet for VRAM planning across quantization formats.

Benchmark Comparison

Benchmarks tell you different things. MMLU tests general knowledge breadth. HumanEval tests code generation accuracy. MT-Bench tests instruction following and conversational quality. Each maps to a different class of production workloads.

Quality Benchmarks (MMLU, HumanEval, MT-Bench)

¹ Llama 4 Scout HumanEval: some sources cite 74.1%, but the official model card does not list HumanEval directly (it reports MBPP at 67.8). Independent evaluations show ~60-61%. Treat all figures as unverified and use your own eval set for production decisions.

² Qwen3-235B-A22B MMLU: sources conflict (87.1 vs 76.6). Verify against the official Qwen3 technical report before citing.

† DeepSeek V3.2 Speciale MMLU and HumanEval: these scores are reported for the DeepSeek V3 base model that Speciale builds on. Speciale-specific benchmark scores are not separately published for general benchmarks. See the DeepSeek V3.2 Speciale deployment guide for task-specific performance data.

° DeepSeek V3.2 Speciale HumanEval 82.6: this score is from HumanEval-Mul (the multilingual variant of HumanEval), not the standard single-language HumanEval benchmark. HumanEval-Mul covers Python, Java, TypeScript, and several other languages. The two benchmarks are not directly comparable.

‡ Llama 4 Maverick HumanEval: some official Meta sources report 86.4%, not 82.4%. The discrepancy likely reflects evaluation methodology differences (pass@1 vs pass@10). Verify against the official Llama 4 model card before citing.

§ Qwen3-32B HumanEval 88.0 and Qwen3-235B-A22B HumanEval 90.3: these scores could not be confirmed from the official Qwen3 technical report or model cards, which focus on EvalPlus and LiveCodeBench instead. Treat as unverified.

MT-Bench scores for all five models could not be confirmed from official model cards or technical reports. Treat the remaining figures as starting points and verify against primary sources or your own benchmarks before capacity planning.

One result stands out on HumanEval. Qwen3-32B reports 88.0§, higher than DeepSeek V3.2 Speciale at 82.6†° (HumanEval-Mul), despite being a much smaller model. These scores are unverified against official sources (see benchmark footnotes). If they hold on your eval set, Qwen3-32B for coding workloads is not a budget compromise; it is the top performer per dollar. MT-Bench scores for all models in this comparison could not be confirmed from official sources and are omitted above. Use your own instruction-following eval for production selection decisions.

Inference Speed on H100 (vLLM, tokens/sec)

The cost formula: (cost/hr) / (throughput tokens/sec x 3600) x 1,000,000 = cost per 1M tokens.

This table is a worked example frozen at the H100 on-demand rate recorded on 14 May 2026 (about $2.50/hr per GPU), and the cost-per-1M column is computed from it. Today Spheron H100 runs $4.16/hr on-demand and $2.21/hr on spot, as of 12 Nov 2026. Spot suits batch workloads or non-latency-sensitive inference, but spot capacity can be reclaimed without notice.

Spot cost-per-token estimates are a worked example frozen at the Spheron spot rates recorded on 14 May 2026: Qwen3-32B on H100 spot ($1.03/hr, ~850 tok/s) ≈ $0.34/M tokens; on B200 spot ($2.12/hr, ~3,000 tok/s est.) ≈ $0.20/M tokens. B200 FP4 at ~12,000 tok/s = ~$0.05/M tokens. Llama 4 Scout on H100 spot ($1.03/hr, ~800 tok/s) ≈ $0.36/M tokens. DeepSeek V3.2 on 8× H100 spot ($8.24/hr, ~400 tok/s) ≈ $5.72/M tokens. Spot capacity can be reclaimed without notice.

Pricing fluctuates based on GPU availability. Spheron rates above are live as of 12 Nov 2026; other providers reflect their most recent published rates and may have changed. Check current GPU pricing → for live rates.

Llama 4 Scout and Qwen3-32B are essentially tied on cost per million tokens at $0.82-$0.87 in that 14 May 2026 worked example. Both run on a single H100 SXM5 and deliver competitive throughput. The choice between them comes down to use-case fit, not cost.

DeepSeek V3.2 Speciale at $13.89 per 1M tokens (same 14 May 2026 rates) is the most expensive option here. That cost is justified only when you need its specific strengths: math reasoning, multi-step logical inference, or tasks where it demonstrably outperforms the alternatives on your eval set.

Memory Requirements

The VRAM numbers in the model overview above are weights-only. At runtime, add 15-20% overhead for activations and framework buffers. For KV cache planning at specific context lengths and batch sizes, see our GPU memory requirements guide and GPU requirements cheat sheet 2026.

The practical cutoffs:

• Single H100 80GB: Qwen3-32B (FP8) or Llama 4 Scout (INT4).
• 4x H100 80GB: Llama 4 Maverick (INT4) or Qwen3-235B (INT4, tight).
• 8x H100 80GB: Qwen3-235B (FP8). DeepSeek V3.2 Speciale (FP8, optimized) fits within the 640 GB aggregate VRAM, though high KV cache usage may still require offloading strategies.

License and Commercial Use

Qwen 3 (Apache 2.0) is the most permissive option. Apache 2.0 has no usage-scale restrictions, no restrictions on using model outputs, and no prohibition on commercial use at any scale. For teams that need maximum flexibility, including using fine-tuned weights as a starting point for other projects, Qwen 3 is the clear choice.

Llama 4 (Llama 4 Community License) permits commercial use with one notable threshold: if your application exceeds 700 million monthly active users, you need explicit written permission from Meta. For the vast majority of production deployments, this is not a constraint. The license also does not restrict using model outputs.

DeepSeek V3.2 (MIT License) is fully permissive with no use-based restrictions. Like Apache 2.0, MIT imposes no limitations on commercial use, output usage, or application domain. DeepSeek switched all V3 variants (including V3.2 Speciale) to MIT licensing starting March 2025.

Use-Case Recommendations

Code Generation

Winner: Qwen3-32B (Qwen3-235B-A22B if budget allows).

Qwen3-32B reports 88.0§ on HumanEval, the highest in this comparison, though this score could not be confirmed from official sources (see benchmark footnotes). Community evaluations and independent benchmarks consistently place it among the top small models for coding. For code completion, generation, and review, Qwen3-32B at about $0.82 per 1M tokens on a single H100 SXM5 (at the 14 May 2026 on-demand rate) is the default choice.

Thinking mode adds on-demand chain-of-thought reasoning for harder problems, debugging sessions, and architecture questions, without changing the deployment configuration. See the Qwen 3 deployment guide for setup details.

Conversational AI and Instruction Following

Winner: Llama 4 Scout for single-GPU cost efficiency. Use Llama 4 Maverick if you need higher quality.

Scout performs well for a single-GPU model on instruction following. Its 10M token context window means you can load entire conversation histories, large documents, or extended tool-use traces without hitting context limits. For most conversational applications, the context window advantage is the defining factor at this price point.

If your application needs Maverick-level quality and 1M token context, budget 4x H100 SXM5, $16.64/hr on-demand on Spheron. See the Llama 4 deployment guide for hardware and vLLM configuration.

RAG and Document Q&A

Winner: Llama 4 Scout.

The 10M token context window changes what RAG looks like. Instead of chunking documents, retrieving fragments, and stitching them back together, you can load entire document sets directly into the context. For long-document Q&A, contract analysis, or codebases with many interdependencies, this eliminates a significant layer of retrieval complexity.

Qwen3-32B (131K with YaRN scaling) and DeepSeek V3.2 Speciale (128K) both have context windows well below Scout's 10M. That handles most RAG applications, but if your documents are longer or you need to retrieve across a large corpus in a single pass, Scout's 10M token window is not available anywhere else in this comparison at this price.

For RAG serving architecture and production configuration, see the vLLM production deployment guide. For the model deployment itself, see the Llama 4 GPU deployment guide.

Reasoning and Math

Winner: DeepSeek V3.2 Speciale.

V3.2 Speciale leads on MMLU (88.5†) and has demonstrated strong mathematical reasoning performance. Press coverage has cited IMO gold-medal level results in 2025, though the precise attribution of this claim to V3.2 Speciale specifically (vs. other DeepSeek variants) is not fully confirmed in primary sources. See the DeepSeek V3.2 Speciale deployment guide for task-specific benchmark details. For math-heavy workloads, scientific reasoning, multi-step logical inference, and tasks where chain-of-thought depth matters, it is the best option here.

The cost is 8x H100, $33.28/hr on-demand on Spheron. Qwen3-235B-A22B is a viable alternative at similar cost if MIT or Apache 2.0 licensing is a priority, and reported HumanEval scores§ suggest it may be the better pick if your reasoning tasks involve code.

Fine-Tuning Ecosystem

DeepSeek V3.2 Speciale supports LoRA and full fine-tuning via Hugging Face transformers. Given the model size (685B parameters), fine-tuning requires a multi-node setup and careful memory management. LoRA is the practical approach for most teams. See our LLM fine-tuning guide for setup details, and the Axolotl vs Unsloth vs torchtune comparison for framework selection.

Llama 4 has strong ecosystem support. unsloth covers Scout efficiently given its manageable size. For Maverick, torchtune is the better fit given its multi-GPU requirements. Both are covered in our Axolotl vs Unsloth vs torchtune post. For the full Scout QLoRA config, dataset prep, and rental cost, see how to fine-tune Llama 4 on a rented GPU.

Qwen 3's Apache 2.0 license means no fine-tuning restrictions of any kind. unsloth supports Qwen3-8B and Qwen3-32B with LoRA out of the box. Standard PEFT/LoRA works with all dense variants. See the LLM fine-tuning guide for configuration details.

How to Deploy on Spheron

The fastest path to running any of these models is a bare-metal GPU instance on Spheron. Here is the setup for Qwen3-32B, the recommended starting point for most teams:

# 1. Provision a 1x H100 instance at app.spheron.ai
# 2. Install vLLM
pip install vllm --upgrade

# 3. Start the inference server
vllm serve Qwen/Qwen3-32B \
  --quantization fp8 \
  --gpu-memory-utilization 0.9 \
  --max-model-len 32768 \
  --port 8000

For DeepSeek V3.2 Speciale (8x H100 required), see the full deployment guide. For Llama 4 Scout and Maverick, see the Llama 4 deployment guide.

Final Recommendation

For most teams starting in 2026, Qwen3-32B is the right default. It leads on code generation, runs on a single H100 SXM5 at $4.16/hr, carries an Apache 2.0 license with no restrictions, and delivers competitive quality on instruction following and general tasks. If you need a 10M token context window for RAG or long-document applications, Llama 4 Scout is the only option at this price point. For math, scientific reasoning, and multi-step inference where raw benchmark quality matters most, DeepSeek V3.2 Speciale is the leader, at 8x the hardware cost ($33.28/hr for 8x H100 SXM5).

If DeepSeek wins your evaluation, decide how to run it before renting hardware: the DeepSeek API pricing vs self-hosted comparison covers where the official endpoint stops being cheaper than your own GPUs. For the latest from Alibaba, our Qwen 3.6 guide explains why the flagship Qwen 3.6 Plus is API-only and covers self-hosting the open Qwen3.6-35B-A3B and Qwen3.6-27B models instead. The Qwen line has continued to evolve since this comparison was written, though the Qwen3.7 generation is API-only; see our Qwen3.7 Max guide for what that means and which open Qwen models to self-host. If low-latency serving matters more than model choice, some of these same open-weight families are also available through Groq's LPU-based API; our Groq API pricing vs GPU rental breakdown covers when that per-token route beats self-hosting on an H100. Fireworks AI serves all three families through its own per-token tiers too, and our Fireworks AI pricing breakdown works out the exact volume where renting the underlying GPU beats its serverless rate. None of these three open-weight models are a fair price comparison against a closed frontier model like Claude Opus, but if that's the alternative you're actually weighing, Claude Opus 4.8 API pricing vs self-hosted LLMs runs the same crossover math against a much higher per-token rate. NVIDIA's own marketplace is worth checking too before you commit to a provider: our DGX Cloud Lepton pricing breakdown covers what the same H100 and H200 capacity these models need costs through Lepton against Spheron's live rates.

DeepSeek V3.2, Llama 4, and Qwen 3 are all available on Spheron with no waitlist. Provision a single H100 for Qwen3-32B or scale to 8x H100 for the full DeepSeek V3.2 Speciale experience.

On-demand H100 → | H200 SXM5 on Spheron → | View all pricing →

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