Llama vs GPT: Benchmarks, cost & deployment (2026)

This guide cuts through the noise with consolidated benchmark scores, a cost model worked at 10M tokens/month, and a decision framework built around five real production scenarios, so you leave with a defensible answer for your next design review.

TL;DR, which model wins for your use case

Llama 4 and Llama 3.x from Meta AI Research win on cost, data-residency, and fine-tuning control (Meta AI Blog: The Llama 4 herd). GPT-5.x from OpenAI wins on out-of-the-box reasoning accuracy and multimodal tasks where prompt engineering iteration budget is tight (Capabilities of GPT-5 on Multimodal Medical Reasoning).

Our engineering teams have swapped GPT-4o for Llama 3.3 70B on two client RAG pipelines where data-residency requirements ruled out third-party APIs, cutting inference cost by 60% at the expense of 18% more prompt-engineering iteration.

At OpenAI prices the base GPT-5 API at $1.25 per 1 million input tokens and $10.00 per 1 million output tokens, with cached input tokens billed at $0.125 per 1 million (TechCrunch (citing OpenAI pricing) and Simon Willison) versus roughly $0.90/million for self-hosted Llama 405B on reserved GPU capacity, the cost equation shifts decisively toward Meta's open-source models at 10M+ tokens per month. GPT-5.x remains the better default for coding tasks, complex instruction-following, and teams without the ops headcount to manage inference infrastructure (OpenAI, Introducing GPT-5).

Current model versions at a glance

Meta AI Research and OpenAI have both refreshed their flagship model lines significantly since 2024. The table below covers the versions your team is most likely to encounter in production or evaluate today, including Llama 4's new Scout and Maverick releases and Claude Opus as the primary closed-source reference point alongside GPT-5.x (Meta AI - The Llama 4 herd: The beginning of a new era).

Llama 405B remains the open-source model that gets used most often as a closed-source parity check, its performance on coding tasks seems competitive with GPT-4-class models according to Meta AI Research's Llama 3 paper. According to Meta's model specifications, Llama 4 Scout's 10M-token context window is the specific data point that changes the deployment equation for long-document retrieval tasks: no model in this table, open or closed, came close to that figure twelve months ago.

Benchmark scorecard: MMLU, HumanEval, GSM8K, LegalBench

Benchmark scores tell part of the story. The table below consolidates the most-cited evals across Llama 3.x / Llama 4, GPT-5.x, and Claude Opus (Artificial Analysis Intelligence Index v4.0). All figures are as-reported by the respective model cards or the Hugging Face Open LLM Leaderboard.

Dates matter here because eval contamination risk rises every quarter new training data is released.

Three things the table doesn't show:

First, when Llama 405B is compared directly to GPT-5.x on MMLU and GSM8K math reasoning, Meta's open-source model has closed the accuracy gap for most general tasks (LLM Stats; BytePlus). Second, coding benchmark performance (HumanEval pass@1) shows GPT-5.x maintaining a lead on complex multi-file tasks, whereas Llama 405B is competitive on single-function synthesis. Third, LegalBench is the eval most vulnerable to contamination: several frontier models were trained on data scraped after the benchmark's public release, so treat those specific scores as indicative rather than definitive. The Llama 405B LegalBench figure in particular lacks a clear third-party source at the time of writing and should be verified against the Hugging Face Open LLM Leaderboard before you use it to guide a specific use case decision.

For teams considering deployment, the choice between models shouldn't hinge on a two-point MMLU delta. What matters is whether benchmark performance on the task category, coding, legal reasoning, or math, translates to your data distribution. The factors that drive real-world accuracy often differ from the factors that drive leaderboard position. Running domain-specific eval sets on client projects before committing to any model swap is a good practice precisely because aggregate leaderboard scores rarely survive contact with production prompts.

Coding: HumanEval pass@1 for Python, JavaScript, and SQL

HumanEval pass@1 scores split sharply by language, and understanding which model leads where provides a clearer basis for choosing between Llama and GPT for specific use cases in software development (Emergent Mind summary of Raihan et al. (2024)).

On Python, GPT-5.x reaches 74.9% pass@1, where chain-of-thought reasoning and tight instruction-following give it an edge on multi-step algorithmic tasks (OpenAI - Introducing GPT‑5). Llama 4 Maverick is reported to perform competitively on Python as well, with figures ranging between 82% and 86% depending on evaluation methodology, though these numbers come from third-party benchmarking rather than a consistent controlled setting (Spheron Network (DeepSeek V3.2 vs Llama 4 vs Qwen 3). Developers comparing the two models for Python workloads should treat those Llama figures as directional rather than definitive until independently verified results are available. That said, the gap between the two families is narrower than the Llama 2 generation showed, which is a meaningful shift (PromptEngineering.org - How Does Llama-2 Compare to GPT-4/3.5 and Other AI Language Models?).

The picture shifts on SQL. Llama 3.3 70B closes to within a few points of GPT-5.x on text-to-SQL benchmarks, an outcome that becomes less surprising when you account for Meta's heavier use of structured data in fine-tuning corpora (Tinybird SQL Benchmark ("Which LLM writes the best analytical SQL?")). However, for teams building internal analytics tooling or data pipeline copilots, that near-parity matters practically. A self-hosted Llama 3.3 70B on vLLM at INT8 quantization can handle SQL generation at roughly one-tenth the per-token cost of GPT-5.x API calls (Developers Digest - "Llama 3.3 70B: Meta's Cost-Effective Frontier Model"). Cost and personal data residency requirements are the two factors that most often tip high-volume SQL workloads toward a self-hosted Llama deployment, where third-party API calls are ruled out by compliance constraints.

JavaScript sits between the two. GPT-5.x retains better pass@1 accuracy on async and callback-heavy patterns (Cirra AI - "GPT-5: A Technical Analysis of Its Evolution & Features"). Llama 4 Scout, the smaller and faster variant, is competitive on straightforward DOM and Node tasks where prompt brevity is the constraint rather than reasoning depth (Box (Evaluating Meta's Llama 4 Models for Enterprise Content with Box AI)). On the HumanEval benchmark overall, Llama 4 Scout achieves a pass@1 score of 81.1% (PricePerToken HumanEval Leaderboard 2026).

For coding copilot deployment, the choice between open-source Llama models and OpenAI's API often comes down to which language dominates the workload, how much weight verification of benchmark claims carries for your team, and whether SQL near-parity justifies the self-hosting overhead.

Math & quantitative reasoning: GSM8K worked examples

GSM8K math reasoning reveals a consistent accuracy pattern between closed and open-source models on multi-step arithmetic word problems when compared across the full benchmark. Here is the same prompt answered by both models, with full step-by-step output included:

Prompt: A factory produces 240 widgets per hour. It runs for 6.5 hours on Monday and 4.75 hours on Tuesday. How many widgets does it produce in total?

GPT-5.x output: Step 1, Monday production: 240 × 6.5 = 1,560 widgets. Step 2, Tuesday production: 240 × 4.75 = 1,140 widgets. Step 3, Total: 1,560 + 1,140 = 2,700 widgets. ✓

Llama 3.3 70B output: Step 1, Monday production: 240 × 6.5 = 1,560 widgets. Step 2, Tuesday production: 240 × 4.75 = 1,140 widgets. Step 3, Total: 1,560 + 1,140 = 2,700 widgets. ✓

Both models provide clear, correct answers on this specific problem type. However, the gap becomes visible on chained conditional problems, where intermediate results feed a second equation. On the full GSM8K benchmark, GPT-5.x scores 95.7% without tool assistance (OpenAI GPT‑5 System Card (PDF), 2025) and Llama 4 Maverick scores 95.0% (Inference Bench, 2025). These factors make both models good choices for quantitative reasoning tasks, and the right pick will depend on specific use case requirements such as deployment constraints and cost.

Latency and throughput under real load

Time-to-first-token is where GPT-5.x and self-hosted Llama models diverge most sharply, and the direction depends entirely on how you deploy. GPT-5.x via the OpenAI API returns a first token in roughly 400-900 ms at standard tier; Llama 3.3 70B on vLLM can beat that at low concurrency, but throughput and latency pull against each other the moment batching kicks in.

The mechanics matter here. vLLM's PagedAttention scheduler fills the KV cache across concurrent requests, which improves tokens-per-second throughput but adds queuing delay to any single request's TTFT. At 512-token prompts, that tradeoff is negligible. At 8k tokens, you're materializing a KV cache entry that can be 2-4 GB for a 70B model at FP16, TTFT climbs fast unless you've sized your A100/H100 fleet for the prompt distribution you actually see in production.

Estimated TTFT by prompt length (self-hosted Llama 3.3 70B on vLLM vs OpenAI GPT-5.x API)

Estimates based on internal Netguru testing; production results vary with GPU SKU, batch size, and API tier.

Groq LPU inference changes the comparison entirely for open-source models. Llama 3.3 70B on Groq returns first tokens in under 150 ms consistently, because the LPU architecture eliminates the memory-bandwidth bottleneck that dominates GPU inference. The catch: Groq's capacity is finite, and rate limits bite harder than OpenAI's at high request volume.

Working with Spendesk, Netguru delivered a high-throughput internal banking system for SEPA payments, where predictable low-latency inference under rate constraints was a core architectural requirement.

For Llama 405B, the throughput story is harder. Multi-node tensor parallelism across two or more H100s is a specific deployment engineering problem, performance gains over the 70B variant are real on long-context tasks, but the infrastructure complexity means most teams use it only when accuracy on complex reasoning tasks justifies the cost. For latency-sensitive applications, the 70B model with quantization (INT8 or INT4 via AWQ) on a single 80 GB H100 is the better practical choice.

Cost comparison: API, managed llama, and self-hosted

Tokens per million pricing is the fastest way to expose how wide the cost gap between closed and open-source models really is, but the headline rate hides infrastructure costs that only appear when you run the math at volume. The factors that matter most are deployment path, monthly token volume, and your team's tolerance for operational overhead.

Open-model endpoint prices move frequently and cluster tightly across Together AI, Groq, and AWS Bedrock — check each provider's live pricing page before finalising a budget. The relative gap to GPT-5.5, not the exact cents, is the durable signal.

10-million-token-per-month worked example

To provide a clear comparison, consider a team running a customer-facing tool at 10 million tokens per month, split roughly 40% input and 60% output. That specific use case produces the following estimated monthly spend across paths:

• GPT-5.5 via OpenAI API: (4M × $5) + (6M × $30) = $20 + $180 = ~$200 per month
• Llama 3.3 70B via a managed open-model endpoint (Together AI, Groq, or Bedrock): at a ~$0.50–1.10/M blended rate, 10M tokens lands in the ~$5–15 per month range — an order of magnitude below GPT-5.5.
• Self-hosted Llama 405B (A100/H100 cluster): at the amortized ~$0.80–1.20/M rate, 10M tokens costs roughly $8–12 per month in compute — but this excludes fixed GPU reservation and engineering overhead that make self-hosting uneconomical below 30–50 million tokens per month.

Groq LPU inference adds a throughput advantage on top of competitive pricing: it is purpose-built for low-latency, high-throughput Llama inference and frequently outperforms GPU-backed managed endpoints on time-to-first-token at moderate concurrency.

Self-hosting Llama 405B on an A100 cluster shifts the equation at scale. GPU reservation and engineering overhead are fixed costs, so the per-token rate drops as volume grows. A two-GPU A100 80GB instance — roughly $2–4 per GPU-hour on major clouds, less on reserved or spot capacity — amortizes to roughly $0.80–1.20 per million tokens at sustained utilization. Below the 30-50 million token threshold, Together AI or AWS Bedrock provide a better fit for the cost profile, with less operational burden.

One cost the table omits is deprecation risk. OpenAI routinely retires older GPT versions within roughly a year or two of a successor's launch, meaning teams building on GPT-5.x should budget for a periodic re-evaluation cycle. Meta's open-source release model means Llama weights persist indefinitely, a good and specific advantage for deployments where stability across a multi-year contract matters more than frontier accuracy. Netguru delivered on exactly this kind of long-horizon project for Careem: developing a more accessible and engaging payment system for Careem Pay.

Fine-tuning: LoRA/QLoRA on llama vs OpenAI fine-tuning API

LoRA/QLoRA fine-tuning on Llama 3.x or Llama 4 gives you direct control over rank, quantization precision, and training data, OpenAI's fine-tuning API gives you none of that, but ships a working model in hours.

The practical difference comes down to three variables:

On a recent document-intelligence project, multi-label classification across a specialized legal taxonomy, our team ran QLoRA at rank 16 on Llama 3.x 8B (INT4) and matched a baseline GPT-5.x zero-shot accuracy while reducing per-query cost by roughly 70% at the volume that project needed. The tradeoff: two days of dataset curation and an afternoon of hyperparameter search that the OpenAI API would have absorbed silently.

Meta's open-source model release model means Llama 405B weights are fine-tunable on the same LoRA toolchain, a customization path that simply does not exist for GPT-5.x. For tasks where proprietary training data is the moat, that deployment control seems more valuable than OpenAI's convenience premium. For teams without MLOps capacity, the API wins on time-to-production. That played out at ARC Europe: 83% reduction in claims processing time (30 to 5 minutes).

Deployment options for llama in production

Llama 3.x and Llama 4 give you five credible production deployment paths, each with a different tradeoff between ops burden, cost, and latency control. The right choice depends on your throughput target, data residency requirements, and how much GPU infrastructure your team wants to own.

VLLM is the choice when throughput is the primary metric. Its continuous batching and PagedAttention KV cache management let a single A100 serve concurrent requests far more efficiently than a naive HuggingFace inference loop, on one recent document-processing engagement, our team moved from a naive serving setup to vLLM and cut p95 latency by roughly 40% at the same batch size. Groq LPU inference offers better raw token-generation speed for latency-sensitive tasks like streaming chat, but at lower batch parallelism.

Together AI and AWS Bedrock suit teams that need managed infrastructure with contractual SLAs and don't want to carry GPU capacity planning. Bedrock specifically adds IAM-native access control and VPC integration, worth the margin premium for regulated data environments. Ollama is not a production deployment path for Llama 405B; its use is limited to local development and smaller models like Llama 3.x 8B. Polpharma API worked with Netguru: How Polpharma leveraged Webflow for rapid deployment, scalability, and easy maintenance.

Decision framework: Which model for which scenario

Choosing between Llama and GPT depends on several factors specific to your project. The decision becomes clearer when you compared them against your actual deployment constraints rather than benchmarks alone.

Choose GPT (OpenAI API) when you need:

• A managed tool that handles growing workloads with minimal infrastructure overhead
• Consistent multilingual support across many languages out of the box
• Rapid prototyping where time-to-value matters more than cost
• Personal data handling is abstracted away by a trusted third-party provider
• Access to multimodal capabilities (vision, audio) without custom integration

Choose Llama when you need:

• Full data sovereignty and on-premises deployment for sensitive workloads
• The ability to fine-tune on proprietary data without sharing it externally
• Lower long-run inference costs at scale once infrastructure is in place
• Transparency into model weights for compliance or audit requirements
• A self-hosted tool that your team controls end-to-end

The nuanced middle ground:

Some scenarios do not provide a clear winner. However, a few patterns hold consistently across production deployments:

The right choice is rarely about which model is objectively good in isolation. It is about which model fits the specific use constraints, budget, and risk tolerance of your organization.

Frequently asked questions: Llama vs GPT