There is no universal winner. Start with DeepSeek-R1 for reasoning-heavy mathematics and difficult coding, Qwen2.5 when you need many model sizes, long context, multilingual or structured output, and Llama when ecosystem support, multimodal checkpoints, or existing Meta tooling is the priority. Make the choice at the exact checkpoint level: licenses, context windows, hardware requirements, serving support and benchmark results differ within every family.
Quick answer: which family fits which job?
The family names are useful only as a first filter. A 7B instruction model, a 70B base model and a multimodal checkpoint from the same family are not interchangeable. Confirm the exact model name, parameter count, instruction tuning, context limit, license and quantization before committing.
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| Primary requirement | Best starting point | Why | Important qualification |
|---|---|---|---|
| Mathematical reasoning and hard coding | DeepSeek-R1 or a current DeepSeek reasoning checkpoint | R1 is trained with large-scale reinforcement learning and is positioned for math, code and general reasoning. | Test latency, verbosity, refusal behaviour and tool-use reliability on your own prompts. |
| Many deployment sizes, from edge to large server | Qwen2.5 | The open family spans 0.5B to 72B parameters and includes small checkpoints and coding-oriented variants. | License terms vary by checkpoint; do not assume the 72B terms match the 0.5B terms. |
| Multimodal input or a broad integration ecosystem | Llama 3.2 Vision or Llama 4, where the selected checkpoint supports the required modality | Meta maintains model cards, a CLI, quantization examples, Llama Stack links and broad platform support. | Downloading requires accepting Meta’s license and acceptable-use policy; Llama 4 has substantial hardware requirements. |
| Long documents | Qwen2.5 or Llama 3.x for commonly listed 128K windows; Llama 4 when its much larger window is genuinely needed | Qwen2.5 and Llama 3.x commonly list 128K context, while Llama 4 lists 10M for Scout and 1M for Maverick. | A larger advertised window still consumes memory and attention compute, and quality can vary across the window. |
| Commercial redistribution or distillation | DeepSeek-R1 is the simplest starting point legally | The R1 release states that code and models are under the MIT License. | Qwen and Meta terms are model-specific; obtain legal review before redistribution or a hosted product. |
Why “open-source LLM” is not one legal or technical category
These projects publish downloadable weights, but “open” does not mean identical freedom. DeepSeek-R1’s release page says, “Code and models are released under the MIT License: Distill & commercialize freely!” Qwen2.5 mixes Apache 2.0, a Qwen Research license and a Qwen license depending on the checkpoint. Llama weights are available to researchers and commercial entities under Meta’s license and acceptable-use policy. Those differences affect redistribution, derivative models, internal use, attribution and prohibited applications.
The Tool Desk
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#1 Best Overall
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DeepSeek: strongest first candidate for reasoning workloads
DeepSeek-R1 and its distill checkpoints
DeepSeek-R1 is the family’s reasoning-focused release. Its official announcement describes performance “on par with OpenAI-o1” and positions the model for mathematics, coding and reasoning after large-scale reinforcement learning in post-training. That statement is a publisher claim, not an independent ranking, but it explains why R1 is often the first model to test on proof-like problems, algorithmic code and multi-step analysis.
The R1 repository lists distilled checkpoints based on both Qwen and Llama: DeepSeek-R1-Distill-Qwen-1.5B, 7B, 14B and 32B, plus DeepSeek-R1-Distill-Llama-8B and 70B. Distillation gives you a choice of sizes and a way to deploy reasoning behaviour where the full model would be impractical. It also means a “DeepSeek” result may depend on the underlying Qwen or Llama architecture, so record the complete checkpoint name in experiments and documentation.
What the published R1 numbers mean
For DeepSeek-R1-Distill-Qwen-32B, the repository reports 72.6 on AIME 2024, 83.3 on AIME 2025, 94.3 on MATH-500, 62.1 on GPQA Diamond, 57.2 on LiveCodeBench and an Arena rating of 1691. DeepSeek-AI published these figures in 2025 for the named evaluation setup. They are useful signals, not a universal leaderboard: changing prompts, number of attempts, tool access, sampling settings, evaluator or model revision can change the result.
Licensing and API option
MIT licensing is comparatively permissive for a model family. It can simplify internal deployment, commercial products and distillation, subject to the actual release notice and your legal obligations. Read the license shipped with the exact artifact rather than relying on the family name.
DeepSeek also publishes an API price example of $0.14 per million input tokens for cache hits, $0.55 per million for cache misses and $2.19 per million output tokens. These figures are volatile and should be rechecked on the current pricing page before budgeting. Hosted API economics can be preferable when GPU capacity, operations or burst traffic matter more than keeping inference on your own machines.
Where DeepSeek is a weaker default
- R1-style reasoning can be slower and more verbose than a compact instruction model for simple extraction or classification.
- Choosing a distill size is a trade-off: a 1.5B or 7B checkpoint is easier to run but will not have the same capability as a 32B or 70B model.
- A benchmark win does not guarantee reliable JSON, tool calls, multilingual responses or low-latency streaming. Validate those behaviours directly.
Qwen2.5: the broadest size and deployment menu
Model range and context
Qwen2.5 includes seven open-sourced models from 0.5B through 72B parameters. Most listed checkpoints support a 128K context and up to 8K generated tokens. That range lets a team use a small model on an edge device, a mid-sized model for ordinary applications and a larger model for demanding analysis without changing families.
Rank #2
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Qwen is a practical shortlist for multilingual work, Chinese-language applications, structured output, long documents and coding variants. The family report describes Qwen2.5-32B-Instruct as strong across most tasks against similarly sized models, but the correct choice still depends on your prompts, latency target and evaluation set.
License differences inside one release
| Checkpoint | License reported for Qwen2.5 | Planning implication |
|---|---|---|
| Qwen2.5-0.5B | Apache 2.0 | Generally familiar permissive terms, but verify the distributed artifact and notices. |
| Qwen2.5-1.5B | Apache 2.0 | Suitable for small deployments subject to normal license compliance. |
| Qwen2.5-3B | Qwen Research license | Do not treat it as equivalent to Apache 2.0; review commercial-use conditions. |
| Qwen2.5-72B | Qwen license | Legal review is required for a commercial or redistributed product. |
The table illustrates why procurement must happen at checkpoint level. A project that is acceptable with a 1.5B model may need a different legal path when it moves to 72B for quality.
Published capability figures
Qwen’s report gives Qwen2.5-72B base scores of 86.1 on MMLU, 58.1 on MMLU-Pro and 86.3 on BBH. Qwen2.5-72B-Instruct scores 83.1 on MATH, 55.5 on LiveCodeBench, 81.2 on Arena-Hard and 9.35 on MT-Bench. Qwen2.5-7B-Instruct scores 75.5 on MATH, 84.8 on HumanEval and 79.2 on MBPP. Qwen Team published these results in 2024 under its stated protocols. Base and instruction-tuned scores should not be mixed, and none of these figures is directly comparable with R1’s numbers unless model versions, prompts, shots and evaluators match.
When Qwen is the sensible default
- You need several model sizes or may move between edge and server deployments.
- Your application must handle Chinese or multiple languages and you want a family explicitly positioned for that breadth.
- You need structured responses or a dedicated coding variant and want to stay within one vendor’s model line.
- You value 128K context but cannot justify the infrastructure required by an extreme context window.
Llama: ecosystem reach, multimodality and platform support
Current families and context windows
Meta’s maintained Llama repository lists Llama 3.1 in 8B, 70B and 405B sizes with 128K context; Llama 3.2 in 1B and 3B sizes with 128K context; Llama 3.2 Vision in 11B and 90B sizes with 128K context; Llama 3.3 in 70B with 128K context; and Llama 4 Scout and Maverick. Llama 4 lists a 10M context for Scout and 1M for Maverick.
Those headline windows do not remove engineering limits. KV-cache memory, attention cost, batching, prompt length and output length all affect throughput. A workload that rarely exceeds 32K tokens may gain little from a million-token maximum, while a document-retrieval system that truly needs very long sequences may value it greatly.
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Llama’s differentiator is the surrounding ecosystem. Meta supplies a maintained CLI, model cards, quantization examples and Llama Stack links, and describes broad support from cloud providers and startups. Existing Llama integrations, monitoring, fine-tuning recipes or multimodal pipelines can reduce migration work even when another family has a higher score on a particular benchmark.
Rank #3
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Llama 3.2 Vision and Llama 4 variants are the candidates to examine when image input or other multimodal behaviour is a requirement. Confirm that the exact checkpoint and serving runtime support the modality; a text-only Llama model cannot be made multimodal merely by changing a prompt.
Hardware example and license workflow
Meta’s repository says Llama 4 requires at least four GPUs for full-bfloat16 inference in its example. With Int4 mixed precision, the documented example puts Llama 4 Scout on two 80GB GPUs in FP8 mode or one 80GB GPU in Int4 mode. This is an operational example, not a guarantee for every batch size, context length or serving stack.
Downloading Llama weights requires accepting Meta’s license and Llama acceptable-use policy. Build that acceptance, notice preservation and prohibited-use review into procurement rather than treating the weights as an unrestricted download.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesHow to read the benchmark tables without choosing the wrong model
Benchmark names look precise but hide experimental choices. A score can depend on the checkpoint revision, base versus instruction tuning, system prompt, few-shot examples, sampling temperature, number of generated attempts, tool access, answer parser and evaluator. Vendor tables are useful signals about what the publisher optimized and measured; they are not proof that one family wins every task.
- Match the task type. MATH and AIME indicate mathematical problem solving; HumanEval, MBPP and LiveCodeBench probe code generation in different ways; MMLU and BBH cover broad knowledge and reasoning; Arena-Hard and MT-Bench involve judged instruction following.
- Match the model type. Compare instruction models with instruction models and base models with base models. A base model may be better for fine-tuning even when it is worse in a chat benchmark.
- Match the operating conditions. Record context length, quantization, hardware, concurrency and whether reasoning traces or tool calls are enabled.
- Use a private acceptance set. Include representative prompts, adversarial cases, long documents, structured-output checks and refusal or safety cases. Measure accuracy, latency, cost and failure recovery, not just a single score.
No single independent, apples-to-apples benchmark covering current DeepSeek, Qwen and Llama releases is established here. Treat cross-family rankings as provisional unless the evaluation protocol is genuinely identical.
Decision guide by use case
Reasoning, mathematics and difficult coding
Begin with DeepSeek-R1 or a current DeepSeek reasoning checkpoint. Compare it with Qwen2.5-32B-Instruct or another size you can operate, and with the Llama checkpoint already supported by your stack. Measure whether longer reasoning improves final answers enough to justify latency and token use. Keep a compact non-reasoning model available for easy requests so every user query does not pay the reasoning cost.
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General coding assistance
Qwen is attractive when you want a dedicated coding variant, multiple sizes and structured output. DeepSeek is the first test for difficult algorithmic or mathematical code. Llama can be the better operational choice when your IDE, gateway, fine-tuning pipeline or observability system is already built around Llama. Evaluate compilation, tests, dependency handling and secure coding—not only HumanEval-style completion.
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Multilingual or Chinese-language products
Start with Qwen2.5 and compare against the exact Llama and DeepSeek checkpoints you can serve. Test terminology, script mixing, translation direction, formatting and safety responses using native-language prompts. A high English benchmark score does not establish quality in another language.
Long documents and retrieval
Qwen2.5 and Llama 3.x commonly list 128K context, which is already sufficient for many retrieval and document-analysis systems. Llama 4’s 10M Scout and 1M Maverick windows are options for unusually long sequences, but test retrieval accuracy at your real prompt lengths and budget for the additional memory and attention work. Chunking and retrieval can still outperform stuffing an entire corpus into one context.
Vision and other multimodal input
Prioritize Llama 3.2 Vision or a Llama 4 variant when the selected checkpoint’s modality matches your application. Confirm image resolution limits, preprocessing, serving support and output format in a small pilot. Text-only Qwen or DeepSeek checkpoints are not substitutes for a vision model without a separate image pipeline.
Commercial redistribution, distillation or hosted products
DeepSeek-R1’s MIT statement is the least complicated starting point among these examples. Qwen2.5-0.5B and 1.5B are reported under Apache 2.0, while 3B and 72B use different Qwen terms. Llama requires Meta’s license and acceptable-use review. Have counsel examine the exact checkpoint, tokenizer, adapters, datasets and distribution method before launch.
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Choose by quantized memory footprint, available GPU count, context length, throughput and serving support—not parameter count alone. A smaller quantized model that meets quality targets can be more private and responsive than a larger model that forces remote API calls or excessive batching.
Best Value
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Local deployment: a practical selection and test process
- Write the workload contract. Specify languages, maximum prompt and output lengths, structured-output schema, tool calls, concurrency, latency target, privacy boundary and acceptable error rate.
- Shortlist exact checkpoints. Record family, size, base or instruct status, modality, context limit, license and release date. Do not write a plan that says only “use Llama” or “use Qwen.”
- Check hardware before downloading. Account for model weights, quantization format, runtime overhead, KV cache and the memory needed by your longest prompt. For Llama 4, use Meta’s four-GPU bfloat16 and 80GB Int4 examples as planning references, not universal guarantees.
- Select a serving stack. DeepSeek’s R1 repository shows vLLM and SGLang serving paths for distill models. Llama supplies CLI and quantization examples. Confirm that your chosen runtime supports the model’s architecture, quantization, streaming, batching and multimodal inputs.
- Run a fixed evaluation harness. Use the same prompts, sampling settings, context lengths and hardware across candidates. Capture exact outputs, latency to first token, tokens per second, peak memory, failures and cost.
- Test production behaviours. Include malformed JSON, long context, multilingual prompts, prompt injection, tool failures, retries, cancellation and model refusal. A model that tops a benchmark but breaks your schema can be the wrong production choice.
- Document the decision. Store model hashes or release identifiers, license text, quantization, system prompt, evaluator version and date. Re-run the suite after model, runtime or hardware changes.
Quantization, context and throughput trade-offs
Quantization reduces the memory needed for weights, allowing a larger checkpoint or a lower-cost GPU configuration. It can also change numerical behaviour, generation quality and compatibility with kernels. Compare the same model in the quantization you will actually deploy; results from full precision do not automatically describe an Int4 production service.
Context length is a capacity limit, not a quality guarantee. Longer prompts increase prefill work and memory use, while long generated answers increase decode time and API cost. For each candidate, test the shortest context that solves the task, then measure degradation as relevant information moves farther from the prompt boundaries.
Parameter count is similarly incomplete. Architecture, active parameters, tokenizer, instruction tuning, distillation, quantization and runtime kernels all affect practical speed and quality. If a model’s documentation does not state an item, mark it as “not stated” in your comparison rather than inferring it from a family name.
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Common mistakes and how to avoid them
Comparing a family instead of a checkpoint
“Llama beats Qwen” is not a reproducible statement. Name the versions, sizes, tuning status, context, quantization and evaluation date. Re-run the comparison when a new checkpoint appears; DeepSeek’s transparency listings, for example, include V4.0 dated April 24, 2026 and V3.2 dated December 1, 2025.
Treating every license as MIT-like
Check the license file and acceptable-use policy shipped with the weights. Preserve notices, confirm redistribution rights and review whether a research-only term applies to your planned use.
Buying hardware for the headline context window
Estimate memory for your real batch size and prompt length. A million-token maximum may be irrelevant to a 16K application, while a 128K model may still require more memory than a small GPU can provide when several users share the service.
Assuming reasoning always improves answers
Reasoning checkpoints can spend more tokens on easy questions and may expose longer intermediate text. Route simple classification, extraction and autocomplete requests to a smaller instruction model, and reserve the reasoning model for cases where its extra work improves the final result.
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Ignoring operational failure modes
- Keep a timeout and retry policy that does not duplicate side effects in tools.
- Validate structured output before passing it to downstream systems.
- Set maximum input and output lengths to control memory and cost.
- Log model version and quantization so a regression can be reproduced.
- Provide a fallback model or human review path for high-impact decisions.
A concise selection checklist
- Have you chosen an exact checkpoint, not only a family?
- Is it base, instruction-tuned, reasoning-distilled or multimodal?
- Does its license permit your internal use, redistribution, hosting or distillation?
- Does the context window cover your real prompts without unacceptable latency?
- Can your GPUs or API budget support the quantization, concurrency and cache?
- Does the serving stack support the architecture, tokenizer, tools and output format?
- Have you tested representative multilingual, coding, long-context and failure cases?
- Did you record benchmark protocol, model revision, hardware and evaluation date?
Bottom line
Choose DeepSeek-R1 first for reasoning-heavy math and coding, Qwen2.5 first for flexible sizing, multilingual and structured-output deployments, and Llama first when multimodality or ecosystem integration is decisive. The winning model is the checkpoint that meets your quality, license, privacy, hardware and latency constraints under your own measured workload.
Quick Recap
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