DeepSeek V3 / R1

Chinese Lab🌐 Open Source Models◆ Well-funded

81%

Overall Score

26 / 32 across 16 capabilities

DETAILS

DeployHuggingFace, DeepSeek API

PricingFree (MIT License)

TargetResearchers, Cost-sensitive

Website↗ deepseek.com

FUNDING & RISK

Funding~$500M

Risk Level◆ Well-funded

DIFFERENTIATOR

MIT license (most permissive); DeepSeek-R1 reasoning rivals OpenAI o1; V3 671B MoE trained for $5.5M (100x cheaper than GPT-4); strongest reasoning in open-source; efficiency breakthrough

CLUSTER SCORES

Benchmarks7/8

Model Features3/4

Deployment8/10

Ecosystem8/10

CAPABILITY BREAKDOWN

Benchmarks

Knowledge (MMLU/GPQA)Full

Performance on knowledge benchmarks — MMLU, GPQA, ARC. Breadth and depth of world knowledge vs frontier closed-source models.

Reasoning (MATH/Logic)Full

Multi-step reasoning, chain-of-thought, MATH benchmark. Dedicated reasoning variants (e.g. DeepSeek-R1, Qwen-reasoning).

Coding (SWE-Bench/HumanEval)Full

Code generation, debugging quality. Specialized code variants (Codestral, Qwen-Coder, Granite Code) and SWE-Bench scores.

Speed & Inference EfficiencyPartial

Tokens-per-second on common GPUs, time-to-first-token, memory efficiency. How fast the model runs on typical self-hosted hardware.

Model Features

Parameter Size RangeFull

Available sizes from small (1-7B) to large (70B+). Ability to match model size to hardware constraints and use case.

Multilingual SupportPartial

Number of languages supported with strong performance. Non-English capability depth and quality.

Deployment

License TermsFull

Apache 2.0 / MIT (fully permissive) vs custom licenses with restrictions (Llama Community License, CC-BY-NC, etc).

Fine-tuning EcosystemPartial

Ease of fine-tuning with LoRA/QLoRA/full. Availability of training recipes, datasets, community adapters, and fine-tune guides.

Quantization SupportFull

GGUF, GPTQ, AWQ, and other quantization formats. Quality retention at lower precision. Community-quantized versions available.

Inference OptimizationFull

vLLM, TGI, llama.cpp, TensorRT-LLM, SGLang support. Framework compatibility and serving infrastructure breadth.

Self-Hosting CostPartial

Cost to self-host. Full = runs on consumer/single GPU (<$1/hr). Partial = needs multi-GPU ($1-5/hr). None = requires GPU cluster ($5+/hr). Considers smallest capable variant.

Ecosystem

Community & EcosystemFull

HuggingFace downloads, GitHub stars, community fine-tunes, tooling support, and overall adoption momentum.

Hosting Platform AccessFull

Available on Together AI, Fireworks, Groq, Replicate, Lepton, and other inference providers. Breadth of cloud hosting options.

Multimodal VariantsPartial

Vision, audio, and video model variants within the family. Multimodal capability breadth (e.g. Llama-Vision, Qwen-VL).

Context LengthFull

Maximum context window. 128K+ is leading. Long-context variants and quality retention at extended lengths.

Safety & ControllabilityPartial

Built-in safety training, system prompt adherence, refusal calibration, alignment quality, and safety documentation.

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