GPT-5.6 Sol costs $4/$20 per million tokens on promo. GPT-6 Astra is $10/$50. Here is the exact volume where open-weight on GPU-hour billing wins, and where it does not.
GPT-5.6 Sol costs $4/$20 per million tokens input/output. GPT-6 Astra is $10/$50. A packet.ai RTX 6000 Pro runs at $0.66/hr. The math is not subtle.
Key takeaways
Most AI teams are still defaulting to the OpenAI API in 2026. That is fine. The SDK is familiar, deployment is fast, and GPT-5.6 Sol is genuinely good. The problem shows up on the invoice. At 100 million output tokens per month, a realistic number for a production RAG pipeline or code assistant, GPT-5.6 Sol costs $2,000. GPT-6 Astra costs $5,000. Teams that have not looked at their token spend in the last 90 days tend to be surprised.
This post looks at the alternative: open-weight models served on GPU hardware, where you pay per GPU-hour rather than per token. The goal is not to talk you out of OpenAI. It is to give you the actual numbers so you can decide where the crossover point is for your workload.
If you want the conceptual background first, the open-weight vs closed-weight models guide on packet.ai covers how they differ in architecture, licensing, and capability.
OpenAI restructured its model lineup in mid-2026. The GPT-5.6 family launched July 9, 2026 across three tiers: Sol (the prior flagship), Terra (mid-tier), and Luna (cheap and fast). GPT-6 Astra arrived September 3, 2026 as the new top-end model. All figures below are per million tokens, pulled from OpenAI's official pricing page and corroborated across multiple independent sources.
Source: OpenAI official pricing documentation, verified September 2026. GPT-5.6 Sol's $4/$20 is promotional. Post-promotion rates are unannounced. Verify current rates at platform.openai.com/docs/pricing before building cost models, as OpenAI updates rates without advance notice.
Two things are worth keeping in mind. First, GPT-5.6 Sol's $4/$20 rate expires at some point after November 21, 2026 at a price nobody has announced yet. The prior generation (GPT-5.5) was priced at $5/$30 before Sol launched. If Sol reverts to something similar, teams scaling aggressively right now on the promo rate will face a 25-50% cost increase with no warning.
Second, GPT-6 Astra's $10/$50 is only the short-context rate. Send a request above 272K input tokens and the entire request reprices at $20 input and $75 output per million. That is not just the tokens above the threshold. That is every token in the request. Repository agents, multi-document RAG pipelines that skip chunking, and long-session agents that accumulate conversation history are all at risk of hitting this ceiling regularly.
Watch out: the Sol promotional pricing cliff
GPT-5.6 Sol's $4/$20 is locked "at least through November 21, 2026" per OpenAI's model page. That is a floor, not a guarantee. If you are building cost models that assume $4 input as a permanent rate, you are pricing on a number OpenAI has not committed to beyond that date.
Open-weight inference works differently from a per-token API. You rent a GPU, load the model, and run it. The token cost is effectively zero once the hardware is running. What you pay is the hourly rate for the GPU, regardless of how many tokens you generate.
That changes the math completely. At low volumes, the API wins: $4/$20 per million is nothing if you only generate a few million tokens a month. Past a certain volume, the GPU wins by a lot. The crossover for most teams is somewhere between 10 and 25 million output tokens per month.
Current packet.ai GPU pricing, verified from packet.ai/pricing:
Source: packet.ai/pricing, verified September 2026. Monthly billing is up to 20% off hourly rates. H100 SXM and H200 are listed as coming soon with no published rate yet.
The RTX 6000 Pro is the card most inference workloads should start with. Ninety-six gigabytes of GDDR7 is enough to fit Llama 3.3 70B or Qwen 2.5 72B in full BF16 on a single GPU, no tensor parallelism, no quantization. At $0.66/hr Dynamic, a team generating 50 million output tokens per month pays around $165 for the compute assuming the card runs about 30% of the time. The same output volume on GPT-5.6 Sol costs $1,000.
packet.ai's RTX 6000 Pro at $0.66/hr fits Llama 3.3 70B in full BF16 precision on a single card and costs $475 per month running continuously, with no per-token charge regardless of how many tokens you generate.
The table below uses GPT-5.6 Sol at $4/$20 and GPT-6 Astra at $10/$50, comparing against an RTX 6000 Pro at $0.66/hr running Llama 3.3 70B. The GPU column shows what continuous 720-hour monthly operation costs, which is the worst case. Real workloads are bursty, so actual GPU spend is often lower.
GPU cost at 720 hours/month continuous: RTX 6000 Pro at $0.66/hr = $475.20/month. Throughput of around 1,800 output tokens per second for Llama 3.3 70B BF16 on an RTX 6000 Pro is an indicative estimate; actual throughput varies by batch size, context length, and concurrency. OpenAI costs show output-only; add input costs to the OpenAI columns for a full comparison. Production workloads typically split 75% input and 25% output by token volume. For a deeper look at the break-even math, see the self-hosting vs API break-even guide.
At 1 billion output tokens per month, GPT-6 Astra costs $50,000. One RTX 6000 Pro running all month costs $475. The quality of Llama 3.3 70B is not the point here. The cost structure of proprietary API billing versus fixed GPU-hour billing is.
The quality gap between GPT-6 Astra and Llama 3.3 70B is real. On complex multi-step agentic tasks, frontier reasoning chains, and requests that genuinely need 500K+ context without degradation, Astra is better. The question is whether that gap matters for your specific workload. For most tasks, it does not.
Classification, RAG, extraction, summarisation, code review, and customer-facing chatbots all fall within what Llama 3.3 70B or Qwen 2.5 72B handle well. Complex agentic pipelines and tasks needing 500K+ context without degradation are the cases where open-weight models genuinely fall short today.
Most teams that switch do not replace OpenAI entirely. They route high-volume, simpler tasks to open-weight models and keep GPT-5.6 Sol or GPT-6 Astra for the 5-10% of requests where capability actually changes the outcome. The LLM routing guide on packet.ai covers how to build that routing layer without much engineering overhead. The AI token cost guide has the per-model cost math if you want to sanity-check the numbers for your specific workload split.
VRAM determines which GPU fits which model. BF16 needs 2 bytes per parameter. INT4 (AWQ or GPTQ) needs around 0.5 bytes. Add roughly 20% headroom on top for KV cache and activation memory. Here is the practical sizing for the most common models against packet.ai's live catalog.
VRAM figures are weights-only. Add ~20% headroom for KV cache and activations. BF16 = 2 bytes per parameter; INT4 (AWQ / GPTQ) = ~0.5 bytes per parameter.
The RTX 6000 Pro's 96 GB is the inflection point. Below that, a 70B model in BF16 requires either quantization or multi-GPU tensor parallelism. At 96 GB, Llama 3.3 70B fits on a single card in full precision, no quantization artifacts, no NVLink fabric, no tensor parallel config to maintain. For teams testing open-weight inference for the first time, one RTX 6000 Pro at $0.66/hr is the lowest-friction starting point.
For the full VRAM math across Llama sizes from 8B to 405B, the Llama GPU sizing guide covers KV cache budgets and throughput estimates per hardware tier.
Neither path is always right. The answer depends on volume, task type, and how much infrastructure you want to own.
Stay on OpenAI when
Switch to open-weight on packet.ai when
The volume threshold is the clearest signal. At 5M output tokens per month, GPT-5.6 Sol costs $100 in output. An RTX 6000 Pro running all month costs $475. The API wins. At 25M tokens, they are roughly even. Above that, the gap grows every month.
The less obvious factor is latency predictability. The OpenAI API is shared infrastructure and p99 latency spikes at peak times. On packet.ai's Dedicated tier, the GPU is yours, so throughput is consistent. If your product has a latency SLA, that matters. The packet.ai vs Groq vs Together AI pricing comparison covers the managed inference API options if you want per-token billing with more predictable latency than OpenAI, without managing the GPU yourself. And if you want to dig further into total cost of ownership across model sizes, the LLM inference cost breakdown has the full picture.
Last reviewed: September 23, 2026. OpenAI pricing changes without advance notice. GPT-5.6 Sol's $4/$20 promotional rate is confirmed until at least November 21, 2026; post-promotion rates are not published. packet.ai GPU pricing verified at packet.ai/pricing. For GPU infrastructure to run open-weight inference at scale, browse available clusters on packet.ai.
Same models. Same API. Fraction of the cost. Start free — no credit card required.
Start Building →
