Meta’s release of the Llama 4 Herd is the latest thing. The announcement introduces three distinct models: Llama 4 Scout (lightest), Llama 4 Maverick, and Llama 4 Behemoth (strongest), each designed for specific use cases while collectively aiming to rival proprietary models like GPT-4o, GPT-4.5, Claude 3.7 Sonnet, and Gemini 2.0/2.5 Pro. Llama 4 Scout and Maverick are immediately available for download on llama.com and Hugging Face. At the same time, Behemoth—which boasts an unprecedented 288 billion active parameters and nearly 2 trillion total parameters—remains in training. Meta projects it to set new benchmarks in STEM fields upon completion. But based on the numbers, what would a Llama 4 comparison with other models look like, you must be wondering?
Let’s tackle that question in this detailed article. We cover the Llama 4 Herd release and compare it with other advanced models, including Claude 3.7 Sonnet, Gemini 2.5/2.0, GPT-4.5/4-o, and more. We also share our insights on how good the new models might be for coding tasks.
Llama 4 Model Details and Specifications
The Llama 4 Herd comprises three models. Each model uses a Mixture-of-Experts (MoE) architecture. This design activates only a subset of parameters per token, optimizing computational efficiency without sacrificing performance. Here’s a detailed breakdown of the specifications and use cases of each:
Llama 4 Scout
- Parameters: 17 billion active, 109 billion total
- Experts: 16
- Context Window: 10 million tokens
- Hardware: Fits on a single NVIDIA H100 GPU (80GB)
- Training Data: Pre-trained on 30 trillion tokens, including text, images, videos, and over 200 languages
- Post-Training: Lightweight Supervised Fine-Tuning (SFT), online Reinforcement Learning (RL), and Direct Preference Optimization (DPO)
- Purpose: Designed for efficiency and accessibility, Scout excels in tasks requiring long-context processing, such as summarizing entire books, analyzing massive codebases, or handling multimodal inputs like diagrams and text.
Llama 4 Maverick
- Parameters: 17 billion active, 400 billion total
- Experts: 128
- Context Window: 10 million tokens
- Hardware: Requires multiple GPUs (e.g., 2-4 H100s depending on workload)
- Training Data: Same 30 trillion token corpus as Scout, with a heavier emphasis on multimodal data
- Post-Training: Enhanced SFT, RL, and DPO for superior general-purpose performance
- Purpose: The workhorse of the herd, Maverick shines in general-use scenarios, including advanced image and text understanding, making it ideal for complex reasoning and creative tasks.
Llama 4 Behemoth (In Training)
- Parameters: 288 billion active, nearly 2 trillion total
- Experts: Undisclosed (likely hundreds)
- Context Window: Expected to exceed 10 million tokens
- Hardware: Requires a large-scale cluster (specifics TBD)
- Training Data: Likely exceeds 50 trillion tokens, with a focus on STEM-specific datasets
- Projected Performance: Meta claims Behemoth will outperform GPT-4.5 and Claude 3.7 Sonnet on STEM benchmarks, though details remain speculative until release.
The training corpus—double that of Llama 3’s 15 trillion tokens—incorporates diverse modalities and languages, enabling natively multimodal capabilities. Post-training techniques like DPO refine response accuracy, reducing hallucinations and improving alignment with user intent, as detailed in the Hugging Face model card.
Llama 4 Comparison with Proprietary Models
Here’s a glance at Llama 4 comparison with proprietary models like:
Gemini 2.5 Pro:
- Strengths: Leads in reasoning (GPQA: 84.0) and coding (LiveCodeBench: 70.4), with a 1M token context and multimodal versatility.
- Gemini 2.5 Pro vs. Llama 4: Outperforms Scout and Maverick in raw scores but lacks their 10M token context; Behemoth may close the gap.
While Gemini 2.5 Pro outperforms Scout and Maverick in raw benchmark scores, its 1M token context window falls significantly short of Llama 4’s 10M context. The upcoming Llama 4 Behemoth may potentially close this performance gap while maintaining the context advantage.
Llama 4 vs Gemini 2.5 Pro Pricing Comparison
Gemini 2.5 Pro’s tiered pricing structure offers competitive rates:
- Shorter prompts (<200,000 tokens): $1.25/million input tokens, $10/million output tokens
- Longer prompts (>200,000 tokens): $2.50/million input tokens, $15/million output tokens.
In contrast, Llama 4 models through Together.ai are priced at:
- Maverick: $0.27/million input tokens, $0.85/million output tokens
- Scout: $0.18/million input tokens, $0.59/million output tokens. (source)
This represents a significant cost advantage for Llama 4, with Scout costing approximately 86% less for input and 94% less for output compared to Gemini 2.5 Pro’s higher tier.
Claude 3.7 Sonnet:
- Strengths: Excels in coding (SWE-Bench: 70.3) and safety, with hybrid reasoning modes; strong in science (GPQA: 84.8).
- Claude 3.7 Sonnet vs. Llama 4: Competitive with Maverick in coding, but smaller context (200K) limits long-form tasks; Behemoth may surpass it.
The model’s hybrid reasoning approach allows it to produce either near-instant responses or extended, step-by-step thinking visible to users, with API users having fine-grained control over reasoning depth.
Context Window and Limitations
Claude 3.7 Sonnet offers a 200K token context window, which while substantial, is only 2% of Llama 4’s 10M token capacity. This limitation significantly impacts Claude’s applicability for extremely long-context tasks like analyzing entire codebases, books, or comprehensive datasets.
Llama 4 vs Claude 3.7 Sonnet Pricing and Cost Optimization
Claude 3.7 Sonnet’s pricing structure:
- Base rate: $3/million input tokens, $15/million output tokens
- Cost optimization options:
- Up to 90% savings with prompt caching
- 50% savings with batch processing
Even with these optimizations, Llama 4 remains more cost-effective for most use cases, with Maverick costing 91% less for input tokens and 94% less for output tokens at base rates.
ChatGPT (GPT-4.5):
- Strengths: Builds on GPT-4o’s multimodal prowess (HumanEval: 90.2) and conversational fluency, with top-tier scores (~89 MMLU).
- GPT-4.5 vs. Llama 4: Outshines Scout and Maverick in general performance; Behemoth’s scale and open-source edge could challenge it.
While GPT-4.5 outperforms Scout and Maverick in raw performance metrics, its context window is only 1.28% the size of Llama 4’s 10M token capacity.
Llama 4 vs GPT-4.5 Premium Pricing Structure
GPT-4.5 commands the highest pricing among all models compared:
- $75/million input tokens
- $150/million output tokens
This premium pricing makes GPT-4.5 significantly more expensive than alternatives:
- A workload with 750K input tokens and 250K output tokens costs approximately $147
- The same workload would cost roughly $0.42 with Llama 4 Scout and $0.63 with Maverick
- This represents a 350x cost premium for GPT-4.5 over Llama 4 Scout
Llama 4 Benchmark Performance
Meta’s extensive benchmarking, published in the model card, provides a clear picture of Llama 4’s capabilities. Below are the results for pre-trained and instruction-tuned variants, followed by detailed comparison sections.
| Category | Benchmark | # Shots | Metric | Llama 3.1 70B | Llama 3.1 405B | Llama 4 Scout | Llama 4 Maverick |
| Reasoning & Knowledge | MMLU | 5 | macro_avg/acc_char | 79.3 | 85.2 | 79.6 | 85.5 |
| Reasoning & Knowledge | MMLU-Pro | 5 | macro_avg/em | 53.8 | 61.6 | 58.2 | 62.9 |
| Reasoning & Knowledge | MATH | 4 | em_maj1@1 | 41.6 | 53.5 | 50.3 | 61.2 |
| Code | MBPP | 3 | pass@1 | 66.4 | 74.4 | 67.8 | 77.6 |
| Multilingual | TydiQA | 1 | average/f1 | 29.9 | 34.3 | 31.5 | 31.7 |
| Image | ChartQA | 0 | relaxed_accuracy | – | – | 83.4 | 85.3 |
| Image | DocVQA | 0 | anls | – | – | 89.4 | 91.6 |
Larger pre-trained Llama 3.1 models generally outperform smaller versions and show competitive results with Llama 4 in reasoning and knowledge benchmarks. Llama 4 Maverick leads in code generation. Multilingual performance is similar across Llama models. In image tasks, only Llama 4 models were assessed, demonstrating strong capabilities in both chart and document understanding.
Here’s what things look like when comparing the Llama 4 class with Gemini, Claude and GPT:
| Category | Benchmark | Metric | Llama 4 Scout | Llama 4 Maverick | Gemini 2.5 Pro | Claude 3.7 Sonnet | ChatGPT 4.5 |
| Image Reasoning | MMMU | accuracy | 69.4 | 73.4 | ~85 (est.) | ~84 (est.) | ~87 (est.) |
| Coding | LiveCodeBench | pass@1 | 32.8 | 43.4 | 70.4 | 70.3 (SWE-Bench) | ~74 (est.) |
| Reasoning & Knowledge | GPQA Diamond | accuracy | 57.2 | 69.8 | 84 | 84.8 | ~85 (est.) |
| Long Context | MTOB (Full Book) | chrF | 39.7/36.3 | 50.8/46.7 | ~60 (est.) | ~55 (est.) | ~62 (est.) |
Instruction-tuned models show varying strengths. While Llama 4 models perform competitively in early image reasoning benchmarks, Gemini 2.5 Pro, Claude 3.7 Sonnet, and ChatGPT 4.5 are projected to excel. In coding, Gemini and Claude currently lead Llama 4, with ChatGPT 4.5 expected to be even stronger. Gemini and Claude also significantly outperform Llama 4 in reasoning and knowledge tasks. For long context understanding, Llama 4 Maverick improves upon Scout, but the other models are anticipated to achieve much higher scores.
Llama 4 Scout Comparison with Llama 3 Models
Llama 4 Scout vs. Llama 3.1 70B
- MMLU: Scout (79.6) slightly edges out Llama 3.1 70B (79.3), despite fewer active parameters, showcasing efficiency gains.
- MATH: Scout (50.3) significantly outperforms Llama 3.1 70B (41.6), reflecting improved mathematical reasoning.
- MBPP: Scout (67.8) beats Llama 3.1 70B (66.4), indicating better code generation.
- Context: Scout’s 10M token window dwarfs Llama 3.1’s 128K, enabling entirely new use cases like full-book analysis.
Llama 4 Maverick vs. Llama 3.1 405B
- MMLU: Maverick (85.5) slightly surpasses Llama 3.1 405B (85.2), despite using fewer active parameters (17B vs. 405B).
- MATH: Maverick (61.2) outshines Llama 3.1 405B (53.5), a notable leap in problem-solving.
- MBPP: Maverick (77.6) exceeds Llama 3.1 405B (74.4), cementing its coding superiority.
- Multimodal: Maverick’s native image processing (e.g., ChartQA: 85.3) adds a dimension absent in Llama 3.1.
Key Improvements
- Training Data: Doubled to 30T tokens from Llama 3’s 15T, enhancing knowledge breadth.
- MoE Efficiency: Fewer active parameters reduce compute costs while maintaining or exceeding performance.
- Context Window: 10M tokens vs. Llama 3’s 128K, unlocking long-context applications.
Llama 4 Behemoth Comparison with Claude 3.7 Sonnet, GPT-4.5, and more
While Meta claims Llama 4 outperforms models like GPT-4o and Gemini 2.0, direct comparisons are limited by proprietary data scarcity. Here’s what we can infer:
- GPT-4o: OpenAI’s simple-evals GitHub reports a HumanEval score of 90.2, but MBPP scores are unavailable. Maverick’s MBPP (77.6) is strong, though likely below GPT-4o’s peak coding performance. On MMLU, GPT-4o’s rumored 87-88 range exceeds Maverick’s 85.5, but Scout and Maverick’s efficiency (single-GPU compatibility) offers a practical edge.
- Gemini 2.5 Pro: Google’s model lacks public benchmark details as of April 2025, but X posts suggest it competes with GPT-4.5. Maverick’s multimodal scores (e.g., DocVQA: 91.6) likely rival Gemini’s, given its focus on image-text integration.
- Claude 3.7 Sonnet: Anthropic’s model excels in reasoning, with rumored MATH scores around 60-65. Maverick’s 61.2 suggests parity or a slight edge, pending Behemoth’s release.
The open-source nature of Llama 4, combined with its competitive performance, challenges the proprietary dominance of these models, though exact comparisons await third-party validation.
Llama 4 Coding Comparison and Capabilities
Llama 4’s coding prowess is a standout feature, driven by its benchmark results and architectural innovations. Here’s a detailed exploration:
Benchmark Highlights
- MBPP: Maverick’s 77.6 pass@1 outperforms Llama 3.1 405B (74.4) and rivals top-tier models, indicating robust code generation.
- LiveCodeBench: Maverick’s 43.4 (vs. Llama 3.1 405B’s 27.7) reflects real-world coding strength, tested on problems from October 2024 to February 2025.
- Context Advantage: Scout’s 10M token window enables it to process entire codebases, debug sprawling projects, or generate context-aware solutions.
Multimodal Coding
Maverick’s ability to interpret visual inputs—like code diagrams or UML charts—enhances its utility. For instance, it can analyze a flowchart and generate corresponding Python code, a capability absent in text-only predecessors.
Developer Accessibility
Scout’s single-GPU compatibility (NVIDIA H100) democratizes access, allowing individual developers to run it locally. Maverick, while more resource-intensive, remains viable for small teams with multi-GPU setups, offering a balance of power and practicality.
Llama 4 Herd Test Prompts
Why rely on benchmarks when you can test Llama 4 Scout and Maverick yourself? Here are some prompts covering general-purpose, writing, and coding tasks that you can use to test their performance. You can compare them with other models like the Claude 3.7 Sonnet here.
2. Summarize the key events and themes of George Orwell’s 1984 in under 150 words.
3. Explain the concept of quantum entanglement in simple terms for a high school student.
5. Implement a simple algorithm in JavaScript that checks if a string is a palindrome.
The Bottom Line
The Llama 4 Herd marks a pivotal moment for open-source AI, delivering models that rival proprietary systems in performance, efficiency, and versatility. Scout and Maverick’s immediate availability empowers developers and researchers, while Behemoth’s anticipated release promises to redefine STEM benchmarks. The coding capabilities—bolstered by high benchmark scores, long context windows, and multimodal features—position Llama 4 as a go-to tool for software development, data analysis, and beyond. Still, more testing is to be done, and it would be exciting to see how good the Llama 4 Behemoth is.
You can try other advanced models like Claude 3.7 Sonnet and DeepSeek R1 here.