Frontier Open Models: Llama 4 Maverick vs DeepSeek V3 vs Qwen 2.5 110B

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The frontier of open-source LLMs has closed the gap with proprietary models faster than most people expected. Three models in particular define what's possible today without a closed API: Meta's Llama 4 Maverick, DeepSeek's V3, and Alibaba's Qwen 2.5 110B. Each comes from a different architecture lineage and excels in different scenarios. If you're trying to pick one -- or understand when to route to which -- this breakdown covers what actually matters.

## Quick Overview

| Model | Architecture | Total Params | Active Params | Context | Best At |
|---|---|---|---|---|---|
| Llama 4 Maverick | MoE | ~400B | ~17B | 1M tokens | Multimodal, long context |
| DeepSeek V3 | MoE | 671B | 37B | 128K tokens | Coding, reasoning, cost efficiency |
| Qwen 2.5 110B | Dense | 110B | 110B | 128K tokens | Multilingual, instruction following |

The most striking difference is the MoE vs dense split. Maverick and V3 are both Mixture-of-Experts models, meaning only a fraction of their total parameters are active for any given token. Qwen 2.5 110B is dense -- all 110B parameters participate in every forward pass. This distinction shapes their benchmark profiles, serving characteristics, and cost structure more than almost anything else.

## Architecture: MoE vs Dense

### How MoE Routing Works

In a Mixture-of-Experts transformer, each MLP layer is replaced by a collection of "experts" (independent feed-forward networks) plus a learned router. For each token at each layer, the router selects a small subset of experts, routes the token through them, and combines their outputs. The total parameter count reflects all experts, but the active parameter count -- the FLOPs you actually pay per token -- is much lower.

Llama 4 Maverick uses 128 experts with top-1 routing, meaning one expert is activated per token per MoE layer. This is aggressive sparsity. The result is roughly 17B active parameters out of ~400B total. DeepSeek V3 uses 256 experts with top-8 routing, keeping 37B parameters active at a time. Both are far more parameter-efficient than a dense model of equivalent capability.

The tradeoff: MoE models require all experts to be resident in memory (or available for fast loading), even though only a small fraction are used per token. A model with 400B total parameters needs roughly 400B worth of memory in weights, even if it runs with the compute budget of a 17B dense model. This makes MoE models memory-hungry but compute-light.

### Dense at 110B

Qwen 2.5 110B activates every parameter for every token. For a 110B model, that means predictable, uniform compute per token. There's no routing overhead, no expert selection latency, and no risk of expert imbalance. The flip side is that every token costs the same -- you can't get MoE's efficiency gains when a given token is "easy" to predict.

Dense models tend to have more uniform inference latency and simpler serving infrastructure. If you're running at high batch sizes and need consistent throughput, a dense model is often easier to optimize than an equivalent MoE.

## Benchmark Comparison

Benchmarks for models at this scale vary depending on how they're prompted, what temperature and sampling settings are used, and whether you're running the base or instruct checkpoint. The numbers below reflect instruct-tuned versions evaluated under the conditions reported in each model's original release.

### General Knowledge (MMLU)

DeepSeek V3 leads the three on MMLU with around 88.5%, a result that was notably competitive with closed models at release time. Llama 4 Maverick scores roughly 85-86%, and Qwen 2.5 110B sits around 85%. The gaps are relatively small in raw percentage terms, but MMLU's breadth means consistent performance across these numbers reflects genuine model quality, not cherry-picking.

### Coding (HumanEval / SWE-Bench)

DeepSeek V3 is the clear winner here. It was specifically optimized for coding, achieving HumanEval pass@1 scores in the high 80s (around 89%), well above what dense models of similar compute cost manage. Qwen 2.5 110B is a capable coder -- around 77% on HumanEval -- and Llama 4 Maverick, while strong on general tasks, was not primarily optimized for code generation.

For production coding use cases like autocomplete, code review, or automated PR generation, DeepSeek V3 has a meaningful advantage.

### Math and Reasoning (GSM8K, MATH)

All three models are strong here. DeepSeek V3 scores around 90% on MATH, Qwen 2.5 110B around 87-88%, and Llama 4 Maverick around 87%. These are all competitive numbers. For math-heavy workloads, any of the three will serve you well, though DeepSeek V3 has a slight edge at the tail of harder problems.

### Multimodal (Vision + Text)

This is Llama 4 Maverick's distinctive territory. It was released as a native multimodal model with strong image understanding built into the architecture -- not bolted on through a separate vision encoder. For tasks that mix visual inputs with language reasoning, Maverick is the only one of the three with first-class support. DeepSeek V3 and Qwen 2.5 110B are text-only.

### Long Context

Llama 4 Maverick's 1M token context window is an outlier. Most inference providers cap it below this due to KV cache memory requirements, but even at 128K or 256K tokens, Maverick maintains strong performance on long-document tasks. DeepSeek V3 and Qwen 2.5 110B both support 128K context and perform reasonably at that length, though long-context behavior can degrade toward the upper end of the window.

## Inference Characteristics

Benchmark scores tell you about quality. Inference characteristics tell you about what you'll actually pay in production.

### Memory Requirements

Serving these models at FP16 (or BF16) requires roughly 2 bytes per parameter:

- Llama 4 Maverick: ~800GB for full-weight FP16 (all experts loaded)
- DeepSeek V3: ~1.3TB for full-weight FP16
- Qwen 2.5 110B: ~220GB for full-weight FP16

In practice, quantization (INT8, INT4) cuts these significantly. At INT4, Qwen 2.5 110B fits comfortably on two H100s with memory to spare for KV cache. Maverick and V3 at INT4 still require multi-node setups or aggressive quantization. Managed inference APIs handle this behind the scenes, but it affects pricing and hardware allocation.

### Tokens Per Second

Because Maverick and V3 have lower active parameter counts relative to their capability level, they can generate tokens faster per unit of compute compared to a dense model at similar benchmark performance. With 17B active params, Maverick can achieve high token throughput if the serving infrastructure can keep experts loaded efficiently. DeepSeek V3 at 37B active params sits between the two.

Qwen 2.5 110B generates tokens at a rate proportional to 110B parameters worth of compute. Dense models at this scale are memory-bandwidth bound: you're spending most of your time moving weights from HBM to compute units, not doing arithmetic. H100s with 3.35 TB/s HBM3e bandwidth help considerably, but there's no architectural shortcut like MoE sparsity.

At high batch sizes, the picture changes. MoE models can have higher routing overhead and cache thrashing when expert selection varies across requests in a batch. Dense models scale more linearly. For throughput-heavy workloads (batch inference, document processing), the compute-vs-memory trade-off shifts in favor of dense models.

### Cost Per Token

At managed inference providers, pricing typically correlates with active parameter count plus infrastructure overhead. Maverick tends to be priced competitively given its active parameter count -- you're getting GPT-4o-class capability at a lower cost-per-token than a full 70B dense model in many configurations. DeepSeek V3 is similarly well-priced for its benchmark tier. Qwen 2.5 110B, being a larger dense model, generally costs more per token than the MoE alternatives at comparable quality for coding and reasoning.

## Choosing Between Them

### Use Llama 4 Maverick When:

- Your application needs multimodal input (images, documents with figures, UI screenshots)
- You have very long contexts -- 128K or more -- and need quality throughout
- You want general-purpose capability close to frontier proprietary models
- You're running a use case where Meta's open license (the Llama Community License) works for your business

### Use DeepSeek V3 When:

- Coding is your primary use case -- autocomplete, code review, debugging, generation
- You need strong math and reasoning performance
- Cost efficiency at scale matters and you want maximum benchmark performance per dollar
- You're fine with the DeepSeek license terms

### Use Qwen 2.5 110B When:

- Multilingual support is important (Qwen 2.5 is particularly strong on Chinese, Arabic, and other non-Latin languages)
- You want predictable, consistent inference without MoE routing complexity
- You're self-hosting and prefer simpler serving infrastructure
- Your workload involves high batch sizes where dense models scale more cleanly

## A Note on Self-Hosting vs APIs

These three models exist at a scale where self-hosting is genuinely expensive. Running Llama 4 Maverick on your own hardware requires a multi-GPU setup with careful expert parallelism. DeepSeek V3 needs even more. Qwen 2.5 110B is manageable on two or four H100s with quantization, but still requires meaningful DevOps investment.

For most teams, the faster path is an OpenAI-compatible inference API that handles hardware allocation, model loading, and quantization. You can swap between models by changing a base URL and model name, run experiments across all three, and pay only for tokens consumed.

If your workload involves these frontier models at sustained high volume (millions of tokens per day), the build-vs-buy calculation shifts and self-hosting starts making more sense. For most applications in early-to-mid scale, API access to these models is both cheaper and faster to iterate on.

## Putting It Together

Llama 4 Maverick, DeepSeek V3, and Qwen 2.5 110B represent genuinely different approaches to building frontier open models. The MoE architectures of Maverick and V3 use parameter sparsity to reach high capability without proportional compute cost. Qwen 2.5 110B takes the straightforward dense path and does it at a scale where the model quality is competitive across most benchmarks.

For most applications with text-only input, DeepSeek V3 is the default recommendation for coding-heavy use cases and Qwen 2.5 110B is a reasonable choice if multilingual coverage or simple serving infrastructure matters. For applications that need vision, long context, or a combination of both, Llama 4 Maverick is the one to evaluate first.

All three are available via General Compute's inference API with OpenAI-compatible endpoints. You can benchmark them on your actual workload in an afternoon, which is a better signal than any third-party comparison table -- latency and quality for your specific prompts will differ from aggregate benchmark numbers.

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Ready to compare them on your workload? General Compute's API supports all three models with no rate limits and low-latency inference. Swap the model name in your existing OpenAI integration and run your eval suite to find the right fit.