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OpenAI vs DeepInfra
OpenAI's closed tiers against the price floor for open-model inference. DeepInfra wins bulk cost-first jobs; OpenAI wins on quality, context and tooling.
By The Subconscious Team · Updated
OpenAI vs DeepInfra: key differences
At the budget end, OpenAI's cheapest tier, GPT-5.6 Luna, costs $0.20 in and $1.20 out per million tokens before discounts. DeepInfra serves DeepSeek V4 Flash at $0.14 in and $0.28 out and small models like Llama 3.1 8B at $0.02, with no minimums or contracts. For bulk extraction, tagging and synthetic data, that gap adds up quickly. OpenAI narrows it with Batch at half price and cached input at 10% of list, and Luna keeps the full 1.05M window that every GPT tier carries.
DeepInfra's savings come with conditions. Much of its price edge comes from quantization, and its FP4 DeepSeek V4 Pro deployment caps context at 66K tokens. Some reviewers report weaker output unless they pin FP8 variants, and there is no managed fine-tuning. OpenAI costs more, but a team gets full context, hosted tools and the Agents SDK. The right answer is often both: DeepInfra for high-volume, low-stakes calls where precision has been checked per model, and OpenAI for customer-facing and multi-tool work.
What OpenAI and DeepInfra do
OpenAI
OpenAI runs the most widely adopted closed-model API. Its September 2026 lineup has GPT-6 Astra at the top for computer use, coding and long agentic runs, priced at $10 in and $50 out per million tokens. Below it sits the GPT-5.6 family: Sol for hard professional work, Terra as the balanced default, and Luna for high-volume jobs at $0.20 in and $1.20 out. All of them carry a 1.05M token context window with up to 128K output.
Example models: GPT-6 Astra, GPT-5.6 Terra
Full OpenAI profileDeepInfra
DeepInfra is the price floor for open-model inference. Developers treat it as the reference point for what a token should cost, with small models like Llama 3.1 8B at $0.02 per million and DeepSeek V4 Flash at $0.14 in and $0.28 out. The catalog covers 150+ open models across text, image and speech behind a fully OpenAI-compatible API. There are no minimums, setup fees or contracts on the shared API.
Example models: DeepSeek V4 Flash, Llama 3.1 8B
Full DeepInfra profileShould you choose OpenAI or DeepInfra?
OpenAI
Choose OpenAI for
- Customer-facing assistants where output quality comes first
- Long prompts well beyond DeepInfra's 66K FP4 cap
- Agents using hosted tools and the Agents SDK
DeepInfra
Choose DeepInfra for
- Bulk tagging, extraction and synthetic data at the lowest token price
- Budget backends for consumer chat apps
- Quick access to new Hugging Face releases
OpenAI vs DeepInfra at a glance
| Attribute | OpenAI | DeepInfra |
|---|---|---|
| Model access | Closed, plus open gpt-oss | Open weights |
| Flagship models | GPT-6 Astra, GPT-5.6 Sol, Terra, Luna | DeepSeek V4 Flash, Llama 3.1 8B |
| Speed | Fast mode: up to 2.5x at 2x price | ~33 tok/s on DeepSeek V4 Pro (FP4) |
| Price | $0.20–$10 in, $1.20–$50 out per 1M | From $0.02 per 1M |
| Customization | N/A | No managed fine-tuning |
| Deployment | API, Azure OpenAI, Bedrock | Shared API, no contracts |
| Long context | 1.05M; 2x input past 272K | 66K on FP4 DeepSeek V4 Pro |
Frequently asked questions
What is the difference between OpenAI and DeepInfra?
OpenAI's closed tiers against the price floor for open-model inference. DeepInfra wins bulk cost-first jobs; OpenAI wins on quality, context and tooling.
When should I choose OpenAI over DeepInfra?
Customer-facing assistants where output quality comes first; Long prompts well beyond DeepInfra's 66K FP4 cap; Agents using hosted tools and the Agents SDK.
When should I choose DeepInfra over OpenAI?
Bulk tagging, extraction and synthetic data at the lowest token price; Budget backends for consumer chat apps; Quick access to new Hugging Face releases.
Is OpenAI or DeepInfra cheaper?
OpenAI: $0.20–$10 in, $1.20–$50 out per 1M. DeepInfra: From $0.02 per 1M. The cheaper choice depends on the model and workload.
Which has more context, OpenAI or DeepInfra?
OpenAI: 1.05M; 2x input past 272K. DeepInfra: 66K on FP4 DeepSeek V4 Pro.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.