Mid-Size Models: Llama 3.1 70B vs Qwen 2.5 72B vs Mistral Large vs DeepSeek-V2 Lite
The 70B parameter range is where open-source models start to match or exceed proprietary options on a wide variety of tasks. These models are large enough to handle complex multi-step reasoning, long-context analysis, and serious code generation, while still being deployable on a single 80GB A100 or a pair of 40GB GPUs. For production teams that need strong performance but cannot justify frontier model pricing, the 70B tier is where most evaluations start.
This post compares four models from this tier: Llama 3.1 70B, Qwen 2.5 72B, Mistral Large 2, and DeepSeek-V2 Lite. These four span different design choices -- dense architecture, MoE architecture, and different training priorities -- and they perform differently enough that picking the wrong one for a workload has a real impact. We cover benchmark numbers across general reasoning, math, and coding, then look at context length and inference throughput, since at this size those factors shape both capability and deployment cost.
The four models
Llama 3.1 70B is Meta's 70 billion parameter model from the Llama 3.1 series. It was trained on 15 trillion tokens and supports 128K context via RoPE scaling. The architecture uses grouped-query attention for efficient inference and is the most widely deployed model in this comparison by a large margin. Llama 3.1 70B has a substantial ecosystem: fine-tunes, quantization configurations, deployment recipes, and framework integrations are all mature. When developers compare 70B models, Llama 3.1 70B is usually the reference point.
Qwen 2.5 72B is Alibaba's 72 billion parameter model from the Qwen 2.5 release. It supports 128K context and was trained with heavy emphasis on coding, math, and structured output generation. Qwen 2.5 72B consistently leads in coding and math benchmarks at this scale. The Qwen team published detailed ablations showing particular attention to instruction following and tool use, and the model is one of the few 70B-class models that reliably handles complex JSON schema outputs without prompt engineering.
Mistral Large 2 is Mistral AI's second-generation large model with 123B parameters. Despite sitting above the strict 70B threshold, it competes directly in this tier: it is positioned as a capable alternative to GPT-4o and Claude 3.5 Sonnet at lower cost, and its inference speed on ASIC infrastructure often lands it in similar price brackets to the true 70B models. Mistral Large 2 supports 128K context, has strong native function calling, and is particularly good at code generation relative to its size. The model was released in July 2024.
DeepSeek-V2 Lite is the smaller variant of DeepSeek's V2 series, using a Mixture-of-Experts architecture with Multi-head Latent Attention (MLA). The model has 16 billion total parameters with 2.4 billion activated per forward pass. That active parameter count puts compute cost well below the dense 70B models in this comparison, though total parameter storage requires more memory. Context window is 32K. DeepSeek-V2 Lite is included here because it competes on inference cost with 70B models despite activating far fewer parameters -- the question is how much capability you give up.
Benchmark results
General reasoning (MMLU)
MMLU covers 57 academic subjects and is the standard measure of broad knowledge and reasoning capability.
| Model | MMLU Score | |---|---| | Qwen 2.5 72B | ~86% | | Mistral Large 2 | ~84% | | Llama 3.1 70B | ~83% | | DeepSeek-V2 Lite | ~58% |
Qwen 2.5 72B and Mistral Large 2 lead, with Llama 3.1 70B close behind. These three are within 3 points of each other and all well above the strong-reasoning threshold most teams would set for production use. DeepSeek-V2 Lite scores considerably lower at 58%, which reflects its much smaller active parameter count. The MoE architecture gives it computational efficiency, but on a knowledge-breadth benchmark like MMLU, it operates more like a capable 7-8B dense model than a 70B one.
Math (GSM8K)
GSM8K tests multi-step grade school math word problems. At the 70B scale, differences are more about training emphasis than raw capacity.
| Model | GSM8K Score | |---|---| | Qwen 2.5 72B | ~96% | | Llama 3.1 70B | ~93% | | Mistral Large 2 | ~93% | | DeepSeek-V2 Lite | ~75% |
Qwen 2.5 72B leads by 3 points over Llama and Mistral, which track closely. The math-focused training in the Qwen 2.5 series is consistent across size buckets -- the same advantage appeared in the 7B comparison. DeepSeek-V2 Lite performs reasonably on math for its active parameter count at 75%, better than its MMLU showing relative to the dense models, but still meaningfully below the 70B tier.
Coding (HumanEval)
HumanEval pass@1 measures the rate at which the first generated solution passes all test cases on 164 Python programming problems.
| Model | HumanEval Pass@1 | |---|---| | Mistral Large 2 | ~92% | | Qwen 2.5 72B | ~86% | | Llama 3.1 70B | ~80% | | DeepSeek-V2 Lite | ~58% |
Mistral Large 2 leads on code with a notable gap over Qwen 2.5 72B. The 92% pass@1 puts it in range of GPT-4o on this benchmark, which is consistent with Mistral's stated focus on code generation in the Large 2 release. Qwen 2.5 72B and Llama 3.1 70B are both strong, with Qwen ahead by 6 points. DeepSeek-V2 Lite's 58% score reflects its smaller active parameter footprint: it handles basic coding problems well but drops off on more complex multi-function problems that require holding more context simultaneously.
Instruction following (MT-Bench)
MT-Bench evaluates multi-turn instruction following across different domains, scored on a 1-10 scale.
| Model | MT-Bench Score | |---|---| | Qwen 2.5 72B | ~9.1 | | Mistral Large 2 | ~9.0 | | Llama 3.1 70B | ~8.8 | | DeepSeek-V2 Lite | ~7.6 |
Qwen 2.5 72B and Mistral Large 2 are essentially tied on instruction following quality. Both models handle complex multi-turn conversations reliably and show good task completion across domains. Llama 3.1 70B at 8.8 is still strong for most tasks. DeepSeek-V2 Lite at 7.6 is noticeably behind -- again, a capability gap that reflects the much lower active parameter count rather than an architectural flaw.
Context length
| Model | Context Window | |---|---| | Llama 3.1 70B | 128K | | Qwen 2.5 72B | 128K | | Mistral Large 2 | 128K | | DeepSeek-V2 Lite | 32K |
The three dense models all support 128K context and handle it usably in practice. Llama 3.1 70B and Qwen 2.5 72B were trained with extended context from the start. Mistral Large 2 also performs well at long context, though like all models at this range, accuracy on needle-in-haystack retrieval degrades somewhat in the 100K+ range. DeepSeek-V2 Lite's 32K limit is adequate for most tasks but rules it out for long-document workflows, large codebase analysis, or extended conversation history that exceeds that window.
Inference speed
The 70B size class is where multi-GPU deployment becomes standard. These estimates are for two A100 80GB GPUs with tensor parallelism at FP16, serving single requests:
| Model | Approx tokens/sec (2x A100 80GB, FP16) | |---|---| | DeepSeek-V2 Lite | ~350-450 | | Mistral Large 2 | ~120-160 | | Qwen 2.5 72B | ~130-170 | | Llama 3.1 70B | ~130-170 |
DeepSeek-V2 Lite is the speed outlier -- because it activates only 2.4B parameters per forward pass, it generates tokens much faster than the dense models even though it requires more memory for the full parameter set. The three dense models cluster together at 130-170 tokens/sec on this hardware configuration.
Mistral Large 2 at 123B actually runs at similar speed to the true 70B models when the comparison is on the same hardware, because its attention mechanism is efficient and modern serving frameworks like vLLM handle it well with tensor parallelism.
On GeneralCompute's ASIC infrastructure, absolute throughput is substantially higher across all four models. The relative ordering holds, but the gap between DeepSeek-V2 Lite and the dense models narrows in practice because the ASIC optimizations improve memory bandwidth utilization differently for dense and sparse architectures.
Summary table
| | Llama 3.1 70B | Qwen 2.5 72B | Mistral Large 2 | DeepSeek-V2 Lite | |---|---|---|---|---| | MMLU | ~83% | ~86% | ~84% | ~58% | | GSM8K | ~93% | ~96% | ~93% | ~75% | | HumanEval | ~80% | ~86% | ~92% | ~58% | | MT-Bench | ~8.8 | ~9.1 | ~9.0 | ~7.6 | | Context | 128K | 128K | 128K | 32K | | Active params | 70B | 72B | 123B | 2.4B | | Relative speed (dense 70B baseline) | 1x | 1x | ~0.9x | ~2.5x |
When to use each model
Use Qwen 2.5 72B for code-heavy or math-heavy applications. It leads on GSM8K and HumanEval among the 70B-class dense models, and its MT-Bench score ties Mistral Large 2 for instruction following quality. The Qwen 2.5 72B training emphasis on structured output also makes it particularly reliable for applications that need consistent JSON or schema-conformant responses without extensive prompt engineering. If you are building a data extraction pipeline, a coding assistant, or a math reasoning tool, this is the 70B model to evaluate first.
Use Mistral Large 2 when code generation quality is the top priority. Its 92% HumanEval pass@1 is notably higher than the true 70B models and competitive with much larger proprietary models. If your application generates complex, multi-function code or needs high first-attempt success rates on coding tasks, the capability premium over Qwen or Llama is real. The tradeoff is a larger model that requires more memory than a true 70B, though inference frameworks handle this well with tensor parallelism across two GPUs.
Use Llama 3.1 70B as the default general-purpose choice. It trails Qwen 2.5 72B and Mistral Large 2 on specialized benchmarks, but it comes with the broadest ecosystem support in this tier: the most fine-tunes, the most deployment documentation, the largest community, and the most thoroughly tested integration paths across frameworks like LangChain, LlamaIndex, and vLLM. If you are evaluating multiple models and want a stable baseline to compare against, Llama 3.1 70B is the natural starting point. It also provides the clearest upgrade path to Llama 3.1 405B if you find you need more capability later.
Use DeepSeek-V2 Lite when inference speed and cost matter more than top-tier quality. The capability gap relative to the dense 70B models is real and consistent across benchmarks -- you are getting roughly 7-9B dense model performance at 70B model memory cost, but with 2-3x faster token generation. This tradeoff makes sense when you need rapid generation for low-latency applications and your task does not require the full capability of a dense 70B, such as rapid classification, summarization of well-structured content, or first-pass drafts in an agentic pipeline that also has a strong revision step. The 32K context limit is a separate constraint that will be binding on some workloads.
Quantization and deployment notes
At 70B, the self-hosting story is different from the 7B tier. A dense 70B model in FP16 requires 140GB of VRAM, which means two A100 80GB GPUs at minimum. Most teams running 70B models self-hosted are on 8xA100 or 4xH100 nodes for practical throughput at production scale.
INT8 quantization brings a 70B model's memory footprint to roughly 70-75GB -- still requiring two GPUs for A100 80GB nodes, but comfortably fitting on a single H100 80GB. Quality regression is minimal, typically under 1-2 points on MMLU and GSM8K.
INT4 quantization via GPTQ or AWQ drops the footprint to roughly 35-40GB, which fits on a single A100 80GB with overhead to spare. Quality regression is more noticeable at this scale than at 7B -- expect 3-5 points on GSM8K and similar on HumanEval. AWQ is generally preferable to GPTQ for maintaining quality on math and reasoning tasks. At INT4, a 70B model on a single A100 runs at roughly 50-70 tokens/sec for single-stream generation.
For DeepSeek-V2 Lite, quantization is more nuanced because the MoE architecture means expert weights are not all active simultaneously. The full 16B parameter model in FP16 requires about 32GB VRAM, and INT4 brings it to roughly 16GB -- making it accessible on consumer A6000 48GB cards or even mid-range 24GB cards with careful management.
vLLM supports Llama 3.1 70B, Qwen 2.5 72B, and Mistral Large 2 natively with paged attention and continuous batching. DeepSeek-V2 Lite support in vLLM is available but requires specific configuration for MoE routing. SGLang also supports all four and is worth benchmarking for latency-sensitive workloads because it handles KV cache management differently in ways that can benefit MoE inference.
Fine-tuning at 70B
Fine-tuning dense 70B models is feasible with QLoRA. A LoRA run on Llama 3.1 70B or Qwen 2.5 72B requires roughly 80-100GB of VRAM with 4-bit base model quantization, which fits on a single H100 80GB or two A100 80GB GPUs. Training time scales with dataset size but is often practical for domain-specific fine-tuning at a few hundred to a few thousand examples.
The benefit of fine-tuning at 70B is that the base model's strong general reasoning is preserved while the model learns a specific style, format, or domain vocabulary. For applications where quality on a narrow task matters and you have labeled data, a fine-tuned 70B often outperforms prompted frontier models at substantially lower inference cost.
Llama 3.1 70B has the most documented fine-tuning recipes, known-good hyperparameters, and community contributions for this size class. Qwen 2.5 72B is a strong choice for code-domain fine-tuning given the base capabilities. Mistral Large 2 is worth considering for applications requiring reliable tool use, given Mistral's focus on function calling in this model.
Conclusion
Among the three dense 70B-class models, Qwen 2.5 72B and Mistral Large 2 lead on specialized benchmarks, and the right choice depends on whether you prioritize coding (Mistral Large 2's clear win), math plus structured output (Qwen 2.5 72B), or ecosystem maturity and breadth (Llama 3.1 70B). The three models are close enough on general reasoning that benchmark differences between Llama and Qwen will often not matter in practice -- what matters more is how each model performs on your specific data and prompt structure.
DeepSeek-V2 Lite belongs in a different evaluation category. If your workload tolerates the capability step-down and you need fast inference at low cost, it is worth testing. But for most applications where teams are evaluating 70B models, the performance gap is too large to ignore.
The practical starting point for most teams is to evaluate Llama 3.1 70B as the baseline and Qwen 2.5 72B as the quality-first alternative, then bring in Mistral Large 2 if code generation quality proves critical. All three respond well to quantization to INT8, which makes self-hosted deployment more accessible without meaningful capability loss.
All four models are available through GeneralCompute's API with an OpenAI-compatible endpoint. You can swap between them with a single parameter change:
from openai import OpenAI client = OpenAI( base_url="https://api.generalcompute.com/v1", api_key="your-api-key", ) # Swap the model string to compare model_id = "llama-3.1-70b-instruct" # Other options: # "qwen2.5-72b-instruct" # "mistral-large-2407" # "deepseek-v2-lite-chat" response = client.chat.completions.create( model=model_id, messages=[ {"role": "user", "content": "Write a Python function to merge two sorted arrays in O(n) time."} ], ) print(response.choices[0].message.content)
See the GeneralCompute model catalog for current pricing, latency benchmarks, and quantization options for each model.