Llama 4 vs DeepSeek V4 vs GPT-5.6: 90% Cost Gap [2026]

Three weeks ago, a single line item on a benchmark tracker started a debate that’s still running through engineering Slack channels: an open-weight model from DeepSeek matched a flagship Claude release on coding accuracy at roughly a tenth of the price. That comparison, and the parallel rise of Meta’s Llama 4 family, has pushed a lot of teams to ask a blunt question. Do you still need a closed, API-only frontier model, or can an open source AI model get the job done for less?

This piece lines up three of the names driving that conversation in September 2026: Meta’s Llama 4 (Maverick and Scout), DeepSeek’s V4 family (Pro 0813 and Flash 0731), and OpenAI’s GPT-5.6 Luna, with reference points from GPT-5.5, GPT-5.5 Pro, and Anthropic’s Claude Opus 5 where the pricing and benchmark gaps matter most. We’ll look at specs, live per-token pricing pulled from multiple trackers, coding and reasoning benchmarks, licensing terms, and what switching actually costs in engineering time. If you’re deciding whether to keep paying for a closed API or move workloads to an open source AI model you can host yourself, the numbers below should settle most of the argument.

Llama 4, DeepSeek V4, and GPT-5.6 Luna: What’s Different in September 2026

The three families sit in genuinely different categories, and that’s the point. Llama 4 Maverick and Llama 4 Scout are Meta’s open-weight releases, distributed under the Llama 4 Community License, meaning you can download the weights, fine-tune them, and run inference on your own infrastructure or through third-party hosts such as AWS Bedrock and Novita. DeepSeek’s V4 line follows the same open-weight playbook the company used with V3, publishing model weights that developers can self-host while DeepSeek and various API resellers also offer hosted inference at a price.

GPT-5.6 Luna, by contrast, is API-only. OpenAI doesn’t publish weights for any GPT-5.x model, so the only way to use Luna, GPT-5.5, or GPT-5.5 Pro is through OpenAI’s hosted endpoints or its enterprise cloud partners. Claude Opus 5 follows the same closed model from Anthropic. That’s the real axis of comparison here: it’s not just performance and price, it’s whether you can ever run the thing yourself.

According to a September 2026 pricing tracker from AI Pricing Guru, GPT-5.6 Luna is currently OpenAI’s cheapest flagship-class model at $0.20 per 1M input tokens, while Meta’s Llama 4 Maverick lists at $0.15 per 1M input tokens and $0.60 per 1M output tokens through hosted providers. That’s a narrower gap than most people expect between a closed frontier model and an open source AI model, which is exactly why the comparison has gotten more interesting this year rather than less.

What’s changed since last year isn’t just the price list. Meta, DeepSeek, and OpenAI have all shipped at least one major update in the past three months, and each update reshuffled where the value actually sits. A team that priced out this comparison in June 2026 would have reached a different conclusion than a team pricing it out today, since GPT-5.6 Luna’s price cut and DeepSeek’s V4 Flash and Pro releases both landed after that point. That’s the trap with any static “best model” ranking in this market: it’s only accurate for as long as it takes a competitor to ship a cheaper or better release, which right now is measured in weeks, not quarters.

Specs at a Glance: Open-Weight vs Closed AI Models Compared

Before getting into benchmarks, here’s how the core specs line up. Pricing figures below come from a mix of first-party rate cards and third-party trackers, since these vendors publish per-host pricing that shifts often. Where a source only gives a range, we’ve used the more conservative (higher) figure.

Model	Developer	Type	Release	Input $/1M	Output $/1M	Composite Benchmark	SWE-Bench Verified	License
Llama 4 Maverick	Meta	Open weight	2025 (current flagship)	$0.15	$0.60	MMLU 85.5%	Not published	Llama 4 Community License
Llama 4 Scout	Meta	Open weight	2025 (current flagship)	$0.08	$0.30	Budget tier	Not published	Llama 4 Community License
DeepSeek V4 Pro 0813	DeepSeek	Open weight	Aug 13, 2026	$1.32	$3.96	96.40%	Not published	DeepSeek Model License
DeepSeek V4 Flash 0731	DeepSeek	Open weight	Jul 31, 2026	$0.44	$1.32	Cost-efficiency tier	88.80%	DeepSeek Model License
GPT-5.6 Luna	OpenAI	Closed, API-only	2026	$0.20	Blended ~$0.45	Cheapest OpenAI flagship	Not published	Proprietary
GPT-5.5	OpenAI	Closed, API-only	Apr 2026	$5.00	$30.00	Quality score 97, Arena Elo 1506	78.6%	Proprietary
GPT-5.5 Pro	OpenAI	Closed, API-only	Apr 2026	$30.00	$180.00	GPQA 94.2%	81%	Proprietary
GPT-5.4 mini	OpenAI	Closed, API-only	2026	Cache $0.07	Not disclosed	Benchmark score 61.11	Not published	Proprietary
Claude Opus 5	Anthropic	Closed, API-only	2026	$5.00	$25.00	97.00% composite	Not published	Proprietary
Claude Opus 4.8	Anthropic	Closed, API-only	2026	Not published	Not published	Prior-gen reference	88.60%	Proprietary

The pattern is clear from row one: every open source AI model in this table publishes weights you can run outside of a vendor’s cloud, and every closed model doesn’t. That single fact drives most of the cost and control decisions later in this article.

Benchmark Performance: MMLU, GPQA, and Arena Elo Scores

Raw benchmark numbers tell a more nuanced story than the pricing table alone. On the LLM Selection Guide maintained by Iternal AI, Llama 4 Maverick posts an Arena Elo (text) score of roughly 1380 and an MMLU score of 85.5%, putting it solidly in the upper tier of general-purpose open source AI models even against several closed competitors from 2025.

On the closed side, the LLM Leaderboard, last refreshed August 25, 2026, has GPT-5.5 Pro leading OpenAI’s own lineup with a GPQA Diamond score of 94.2% and an SWE-Bench Verified score of 81%, both at a 1M-token context window. The standard GPT-5.5 trails slightly at GPQA 93.6% and SWE-Bench 78.6%, but costs six times less per input token ($5 versus $30 per 1M), which is the kind of tradeoff that makes procurement conversations complicated.

Anthropic’s Claude Opus 5 tops a separate ranking from ofox.ai, updated September 6, 2026, with a 97.00% composite benchmark score and an AA Intelligence index of 63.1, edging out GPT-5.5 Pro on that particular composite metric while costing roughly a sixth as much per output token. That same ranking places DeepSeek V4 Pro 0813 second overall at 96.40%, a gap of less than one point from the top closed model, at a fraction of the price. When a fully open source AI model lands within a point of the best closed model on a composite benchmark, “good enough to switch” stops being a hypothetical.

None of these scores should be read as absolute truth. Benchmark leaderboards use different test sets, different prompting strategies, and different update cadences, which is why we’ve pulled numbers from three independent trackers rather than one. The consistent thread across all three: Llama 4 and DeepSeek V4 close most of the quality gap that used to separate open and closed models, and in some categories, they’ve erased it.

It’s also worth flagging what these composite scores don’t capture. A single percentage figure averages performance across categories that matter very differently depending on the task in front of you. A support-ticket summarizer doesn’t care whether a model can solve graduate-level physics problems, and a model tuned for GPQA Diamond isn’t automatically the best choice for extracting structured data from a scanned invoice. Treat every score in this article as a starting filter for a shortlist, then validate against your own prompts before signing a contract or provisioning GPU capacity around any single number.

SWE-Bench and Coding Performance Compared

For engineering teams, SWE-Bench Verified is the number that actually moves budget decisions, since it measures whether a model can resolve real GitHub issues rather than answer trivia. The ofox.ai ranking puts DeepSeek V4 Flash 0731 at 88.80% on SWE-Bench Verified, priced at $0.44 per 1M input tokens and $1.32 per 1M output tokens. That edges out Claude Opus 4.8’s 88.60% on the same metric, at what the same report describes as roughly a tenth of Claude’s per-token cost.

GPT-5.5 Pro still leads outright on SWE-Bench Verified at 81% among the closed models tracked by LLM Leaderboard, but that comparison sits at a different benchmark version and pricing tier than the DeepSeek and Claude figures above, since leaderboards revise their SWE-Bench harness periodically. The practical takeaway holds regardless of which exact harness a tracker used: a self-hostable, open source AI model from DeepSeek is now competitive with, and by some measures ahead of, a previous-generation Claude flagship on real-world coding tasks, at a much lower cost per token.

Llama 4 Maverick and Scout don’t have a published SWE-Bench Verified score in the trackers used for this piece, which is itself a useful signal. Meta has leaned harder into general reasoning, multimodal input, and long-context retrieval with the Llama 4 family than into coding-specific optimization, so teams building developer tools have gravitated toward DeepSeek V4 or GPT-5.5-class models instead, while Llama 4 sees more traction in customer-facing chat and document-processing workloads.

Pricing Breakdown: What Each Model Actually Costs

Sticker price per million tokens is the number everyone quotes, but it hides a lot of variance by host. Llama 4 Maverick, for example, prices at $0.15 per 1M input tokens and $0.60 per 1M output tokens through the Novita host tracked by AI Pricing Guru, but at $0.24 input and $0.97 output when run through AWS Bedrock, according to a separate tracker from Layer3Labs. Llama 4 Scout shows the same pattern: $0.08/$0.30 on one tracker, $0.17/$0.66 on Bedrock. Self-hosting removes the host markup entirely, but it shifts the cost into GPU rental or capital spend instead.

Model	Cheapest tracked host	Input $/1M	Output $/1M	Enterprise host (e.g. AWS Bedrock)	Notes
Llama 4 Maverick	Novita	$0.15	$0.60	$0.24 / $0.97	Self-hosting avoids host markup
Llama 4 Scout	Third-party API	$0.08	$0.30	$0.17 / $0.66	Meta’s budget-tier model
DeepSeek V4 Flash 0731	DeepSeek API	$0.44	$1.32	Varies by reseller	Best SWE-Bench per dollar in this set
DeepSeek V4 Pro 0813	DeepSeek API	$1.32	$3.96	Varies by reseller	Near-flagship quality, fraction of the price
GPT-5.6 Luna	OpenAI API	$0.20	Blended ~$0.45	OpenAI only	Cut from $2.25 blended in one 80% price move
GPT-5.5	OpenAI API	$5.00	$30.00	OpenAI only	1M context, quality score 97
Claude Opus 5	Anthropic API	$5.00	$25.00	Anthropic, AWS, GCP	Top composite score at 97.00%

The single biggest pricing story of the quarter, according to a September 11, 2026 update from BenchLM’s pricing tracker, is that OpenAI cut GPT-5.6 Luna’s blended price from $2.25 to $0.45 per 1M tokens, an 80% reduction. That move alone reframes a lot of build-versus-buy math, since it puts a closed frontier-adjacent model within striking distance of open source AI model pricing without requiring anyone to manage GPU infrastructure.

Total Cost of Ownership: API Calls vs Self-Hosted Inference

Per-token pricing only tells half the story once volume gets serious. A team processing 500M input tokens and 100M output tokens a month against Claude Opus 5’s rate card ($5.00/$25.00 per 1M) would pay roughly $2,500 for input and $2,500 for output, for a monthly API bill near $5,000. The same volume against GPT-5.6 Luna’s post-cut blended rate of $0.45 per 1M tokens would run closer to $270 a month, an enormous difference that has nothing to do with self-hosting at all, just OpenAI’s own pricing strategy.

Self-hosting an open source AI model like Llama 4 Maverick or DeepSeek V4 Flash removes per-token billing entirely, replacing it with GPU rental or ownership costs. A single high-memory GPU instance capable of serving a 70B-class model at reasonable latency typically runs into the low thousands of dollars per month on cloud rental pricing, which only pencils out below the API-equivalent cost once request volume climbs into the hundreds of millions of tokens monthly, or once data residency requirements make API calls to a third party a non-starter regardless of price. Below that volume threshold, paying per token for a hosted open source AI model, or simply using a cut-rate closed model like GPT-5.6 Luna, is usually cheaper once engineering time for MLOps, monitoring, and scaling is factored in.

The honest conclusion here isn’t “self-host everything.” It’s that the price gap that used to justify self-hosting almost by default has narrowed since GPT-5.6 Luna’s price cut, while the quality gap that used to justify staying on a closed model has narrowed thanks to DeepSeek V4 and Llama 4. Both trends point the same direction: check the math for your specific volume before committing either way.

There’s a second, less obvious cost that rarely makes it into these spreadsheets: staffing. Running an open source AI model in production means someone on the team owns model updates, GPU driver patches, autoscaling policy, and incident response when the inference server falls over at 2 a.m. That’s not a reason to avoid self-hosting, but it is a reason to price it honestly. If the fully loaded cost of a part-time MLOps engineer is $60,000 a year and your projected API savings from self-hosting are $40,000 a year, the open-weight route is a net loss until volume grows, no matter how good Llama 4 Maverick’s benchmark scores look on paper.

Licensing and Deployment: Can You Actually Run These Yourself?

Licensing terms matter more than most comparison articles admit. Llama 4 ships under the Llama 4 Community License, which permits commercial use, fine-tuning, and redistribution for most companies, with a notable restriction: businesses with more than 700 million monthly active users must request a separate license from Meta. For the overwhelming majority of startups and mid-size companies, that clause is irrelevant, but it’s worth knowing it exists before you build a product roadmap around an open source AI model you assume has zero strings attached.

DeepSeek publishes its V4 models under its own model license, which similarly allows commercial deployment and self-hosting. In both cases, “open weight” doesn’t mean public domain, and legal review of the exact license text is worth the hour it takes before shipping either model into a regulated industry.

GPT-5.6 Luna, GPT-5.5, GPT-5.5 Pro, and Claude Opus 5 carry no self-hosting option at all. You use them through OpenAI’s or Anthropic’s endpoints, subject to their terms of service, rate limits, and data handling policies. For companies in finance, healthcare, or government where data residency rules block sending prompts to a third-party API, that’s not a pricing question, it’s a compliance question, and it’s often the real reason teams migrate to an open source AI model even when the closed option is technically cheaper per token.

Reasoning, Math, and Long-Context Capabilities

Long-context handling and multi-step reasoning separate these models more than raw chat quality does. GPT-5.5 and GPT-5.5 Pro both support a 1M-token context window, according to LLM Leaderboard’s August 2026 update, putting them in the same context class as Llama 4 Maverick’s 1M-token window reported by AI Pricing Guru. That parity matters for document-heavy use cases like legal review or codebase-wide analysis, where truncating context loses information the model needs.

On math and graduate-level reasoning, GPT-5.5 Pro’s GPQA Diamond score of 94.2% currently leads the closed-model group tracked here, narrowly ahead of GPT-5.5 at 93.6%. Claude Opus 5’s 97.00% composite score from ofox.ai isn’t a direct GPQA comparison since it blends multiple benchmark categories, so treat the two numbers as measuring related but not identical things. DeepSeek V4 Pro 0813’s 96.40% composite score sits close enough to Claude’s that the practical difference on most day-to-day reasoning tasks is unlikely to be noticeable, especially given the price gap between the two.

The GPT-5.4 mini data point is worth flagging separately. BenchLM’s pricing and benchmark calculator lists it with a 400K context window, a benchmark score of 61.11, and a score-per-dollar rating of 13.6, the highest efficiency rating in that specific comparison. It’s not a frontier model on raw capability, but for classification, extraction, and simple retrieval tasks where a 1M-context flagship is overkill, it’s a legitimate budget option that some teams overlook while debating Llama 4 versus DeepSeek versus GPT-5.5 Pro at the top of the market.

Real-World Use Cases: Who Should Use Which Model

The right pick depends heavily on workload shape, compliance needs, and in-house infrastructure skill. Here are five scenarios that map cleanly onto the data above.

A fintech company screening transactions for fraud patterns at high volume, with a strict no-third-party-data rule, is a strong candidate for self-hosting DeepSeek V4 Flash 0731 or Llama 4 Scout behind an internal API, since neither prompt nor completion data ever leaves the company’s own infrastructure.
An indie game studio generating NPC dialogue and quest text on a tight budget will usually get more value from Llama 4 Scout’s $0.08/$0.30 pricing than from a flagship closed model, since dialogue generation rarely needs GPT-5.5 Pro-level reasoning.
A SaaS company building an AI coding assistant feature should weigh DeepSeek V4 Flash’s 88.80% SWE-Bench Verified score against Claude Opus 5’s higher composite score and decide based on whether the roughly 10x price difference outweighs the quality gap for their specific customer base.
A university research lab handling sensitive or grant-restricted data often has no choice but an open source AI model it can audit and run on institutional hardware, making Llama 4 Maverick’s Community License and 1M-token context window a practical fit for large-document analysis.
A marketing agency producing high volumes of client copy with no in-house MLOps team is usually better served by GPT-5.6 Luna’s low per-token cost and zero infrastructure overhead than by any self-hosted alternative, since the engineering cost of running your own inference stack would erase the savings.
An enterprise with existing AWS commitments may prefer Llama 4 Maverick or Scout hosted directly on AWS Bedrock, accepting the higher per-token markup in exchange for staying inside an already-approved compliance and billing perimeter.

Code Example: Calling Each Model’s API

The request shape differs slightly across providers, but the pattern is consistent enough that switching models mid-project is mostly a matter of changing the endpoint and auth header. Here’s a minimal Python example showing the same prompt sent to a self-hosted Llama 4 endpoint versus OpenAI’s GPT-5.6 Luna:

# Self-hosted Llama 4 Maverick via an OpenAI-compatible inference server (e.g. vLLM)
import requests

response = requests.post(
    "http://localhost:8000/v1/chat/completions",
    json={
        "model": "llama-4-maverick",
        "messages": [{"role": "user", "content": "Summarize this support ticket in one sentence."}],
        "max_tokens": 200
    }
)
print(response.json()["choices"][0]["message"]["content"])

# GPT-5.6 Luna via OpenAI's hosted API
import openai

client = openai.OpenAI(api_key="YOUR_OPENAI_KEY")
response = client.chat.completions.create(
    model="gpt-5.6-luna",
    messages=[{"role": "user", "content": "Summarize this support ticket in one sentence."}],
    max_tokens=200
)
print(response.choices[0].message.content)

Because most self-hosted open source AI model deployments run behind an OpenAI-compatible server like vLLM or TGI, the client-side code barely changes between a local Llama 4 deployment and a hosted GPT-5.6 Luna call. The real engineering work happens in provisioning, scaling, and monitoring the inference server itself, not in the API contract.

Migration Guide: Moving From a Closed API to an Open-Weight Model

Switching an existing product from a closed API to a self-hosted open source AI model is a project, not a config change. Here’s a realistic path.

Audit current token volume and cost from your OpenAI or Anthropic billing dashboard over the last three months to establish a real baseline, not an estimate.
Identify which specific endpoints or features are driving the bulk of spend, since most teams find 80% of cost concentrated in a handful of high-volume call sites.
Shortlist candidate open-weight models (Llama 4 Maverick/Scout, DeepSeek V4 Pro/Flash) based on the benchmark categories that matter for those specific call sites.
Run a side-by-side evaluation using your own production prompts, not public benchmark sets, since real user inputs behave differently than curated test sets.
Choose an inference serving stack (vLLM, TGI, or a managed host like AWS Bedrock) based on your team’s existing GPU and Kubernetes experience.
Provision GPU capacity sized for your p95 latency requirement, not your average load, since inference serving degrades badly under burst traffic.
Stand up the new model behind a feature flag so traffic can be routed gradually rather than cut over all at once.
Route 5% of production traffic to the new model and compare output quality, latency, and error rate against the incumbent for at least a week.
Build automated evaluation checks (accuracy, refusal rate, format compliance) so regressions get caught before a full rollout, not after.
Ramp traffic in stages (5% to 25% to 50% to 100%) while watching cost per request drop and confirming quality holds.
Decommission the old API integration only after a full billing cycle has passed on the new model, to confirm cost savings materialize as projected.
Revisit the comparison quarterly, since both open and closed pricing have moved sharply in 2026 and a decision made in Q1 may not hold by Q4.

Most teams underestimate step six. Provisioning GPU capacity for a self-hosted open source AI model is the single biggest source of surprise cost and delay in these migrations, and it’s the step that makes “just switch to the open model” a much bigger decision than a pricing table alone suggests.

Ecosystem and Tooling Support

A model is only as useful as the tooling built around it, and this is one area where the gap between open and closed options still shows up clearly. GPT-5.6 Luna, GPT-5.5, and Claude Opus 5 plug directly into mature SDKs, function-calling frameworks, and observability platforms that have had years to mature around OpenAI’s and Anthropic’s API conventions. Most agent frameworks, retrieval-augmented generation libraries, and prompt-management tools support these APIs on day one of a new release.

Llama 4 and DeepSeek V4 benefit from a different but increasingly strong ecosystem: open-source inference servers like vLLM and Text Generation Inference (TGI) added support for both model families quickly after release, and because these servers expose OpenAI-compatible endpoints, most existing agent frameworks work against a self-hosted open source AI model with only a base URL change. The gap that remains is less about compatibility and more about operational maturity: fine-tuning pipelines, quantization tooling, and multi-GPU serving configurations for Llama 4 and DeepSeek V4 are improving fast, but a team new to self-hosting will still hit more rough edges than a team calling a hosted API for the first time. Factor that learning curve into any migration timeline rather than assuming day-one parity with a service you’ve been calling for years.

Pros and Cons of Llama 4, DeepSeek V4, and GPT-5.6 Luna

Llama 4 Maverick and Scout

Pros: Fully open weights, Llama 4 Community License permits most commercial use, competitive MMLU and Arena Elo scores, 1M-token context on Maverick, lowest per-token pricing in this comparison for Scout.
Cons: No published SWE-Bench Verified score, 700M MAU licensing carve-out for the largest companies, self-hosting requires GPU infrastructure and MLOps skill.

DeepSeek V4 Pro 0813 and Flash 0731

Pros: Near-flagship 96.40% composite score on Pro, SWE-Bench Verified of 88.80% on Flash beating a previous-gen Claude model, both open weight and self-hostable, strong price-to-performance ratio.
Cons: Higher per-token price than Llama 4 Scout, less third-party hosting availability than Meta’s models, newer releases (July/August 2026) mean less production track record.

GPT-5.6 Luna

Pros: Lowest blended price of any OpenAI flagship after an 80% cut, zero infrastructure to manage, backed by OpenAI’s uptime and support.
Cons: No self-hosting option, subject to OpenAI’s terms and rate limits, data leaves your infrastructure on every call.

Which Model Should You Choose in 2026?

If your workload is high-volume, cost-sensitive, and you already run Kubernetes or manage GPU fleets, an open source AI model from the Llama 4 or DeepSeek V4 family is the better long-term bet, since the per-token savings compound with scale and you keep full control over data residency. If your team is small, ships fast, and doesn’t want to own inference infrastructure, GPT-5.6 Luna’s post-cut pricing removes most of the financial argument for going the self-hosted route at all.

Coding-specific products should look hardest at DeepSeek V4 Flash 0731 given its SWE-Bench Verified lead over Claude Opus 4.8 at a fraction of the cost, while document-heavy, long-context workloads fit Llama 4 Maverick’s 1M-token window well. Teams that need the single highest composite benchmark score regardless of cost still have reason to reach for Claude Opus 5 or GPT-5.5 Pro, particularly in regulated industries where the vendor’s compliance certifications matter as much as the model’s raw output quality.

The Verdict: Llama 4 vs DeepSeek V4 vs GPT-5.6 Luna

The data points to a genuine shift rather than a marginal one. An open source AI model from DeepSeek now beats a recent Claude flagship on SWE-Bench Verified (88.80% versus 88.60%) at roughly a tenth of the price, and Meta’s Llama 4 Maverick posts an Arena Elo score competitive with several closed models while costing $0.15 per 1M input tokens. At the same time, OpenAI’s 80% price cut on GPT-5.6 Luna shows that closed-model vendors aren’t standing still on cost either.

Zoom out and the bigger story is competitive pressure, not any single model winning outright. Three different companies, working from three different strategies (open-weight community licensing at Meta, open-weight commercial licensing at DeepSeek, and aggressive API price cuts at OpenAI) have all converged on roughly the same conclusion in the same quarter: per-token cost is now a primary battleground, not an afterthought. For anyone building on top of these models, that’s good news regardless of which one you pick, since the pressure each company puts on the others tends to show up as your next quarterly bill going down rather than up.

There’s no single winner here, and any comparison claiming otherwise is oversimplifying. For raw coding economics, DeepSeek V4 Flash 0731 is the standout. For long-context, self-hostable general use, Llama 4 Maverick fits. For teams that want frontier-adjacent quality with zero infrastructure burden, GPT-5.6 Luna’s new pricing makes it the path of least resistance. Run your own volume numbers against the tables above before committing, since the right answer changes as soon as your token volume, compliance requirements, or in-house GPU access change.

Frequently Asked Questions

Is Llama 4 free to use commercially?

Yes, for most companies. The Llama 4 Community License permits commercial use and fine-tuning, with one carve-out: companies with more than 700 million monthly active users must request a separate license from Meta. Below that threshold, an open source AI model like Llama 4 Maverick or Scout can be deployed commercially at no licensing cost beyond your own infrastructure spend.

Which model is cheapest for high-volume applications?

On a pure per-token basis, Llama 4 Scout at $0.08/$0.30 per 1M tokens and GPT-5.6 Luna’s post-cut blended rate of roughly $0.45 per 1M tokens are the two cheapest options covered here. Which one is actually cheaper for your workload depends on whether you self-host (removing per-token billing but adding GPU cost) or use a hosted API.

Can I self-host DeepSeek V4?

Yes. DeepSeek publishes V4 Pro 0813 and V4 Flash 0731 as open-weight models under DeepSeek’s own model license, which permits self-hosting and commercial deployment, similar to how the company released its V3 generation.

Is GPT-5.6 Luna better than Claude Opus 5 for coding?

The trackers used in this comparison don’t show a published SWE-Bench Verified score for GPT-5.6 Luna specifically, so a direct coding comparison against Claude Opus 5’s 97.00% composite score isn’t available from current data. For coding-specific benchmarking, GPT-5.5 Pro (81% SWE-Bench Verified) and DeepSeek V4 Flash 0731 (88.80%) have published, comparable figures.

What context window does each model support?

GPT-5.5 and GPT-5.5 Pro both support a 1M-token context window, matching Llama 4 Maverick’s reported 1M-token window. GPT-5.4 mini supports a smaller 400K-token window. Context window figures for DeepSeek V4 Pro/Flash and Llama 4 Scout were not published in the trackers used for this comparison.

Do I need a GPU cluster to run Llama 4 Maverick myself?

Yes, self-hosting any model in the Llama 4 Maverick class requires high-memory GPU infrastructure, whether that’s on-premise hardware or a rented cloud GPU instance. For teams without existing MLOps capacity, using a hosted provider like AWS Bedrock or Novita (which still runs the same open source AI model weights, just managed for you) is usually the faster path to production.

Which model scores highest on SWE-Bench Verified in this comparison?

Among the models with a published SWE-Bench Verified score in the sources used here, GPT-5.5 Pro leads at 81%, followed by DeepSeek V4 Flash 0731 at 88.80% and Claude Opus 4.8 at 88.60% in a separate tracker. Because these come from different leaderboards with potentially different test harness versions, treat cross-tracker comparisons as directional rather than exact.

Is DeepSeek V4 safe to use for enterprise data?

That depends on your deployment method and your organization’s specific compliance requirements. Because DeepSeek publishes V4 as an open-weight model, enterprises with strict data residency rules can self-host it entirely within their own infrastructure, avoiding any third-party data transmission, an option that isn’t available with closed models like GPT-5.6 Luna or Claude Opus 5. Enterprises should still run their own security and compliance review before deployment, regardless of which model they choose.