DeepInfra API Review 2026: 50+ Models at Industry-Low Pricing
DeepInfra launched in 2022 with a singular focus: making open-source LLM inference as cheap as possible. While Groq chased speed records and Cerebras built custom silicon, DeepInfra bet on a more conventional but ruthlessly optimized infrastructure stack — high-end NVIDIA GPUs (H100, H200, A100) running a custom inference engine that minimizes token-time costs.
TL;DR: DeepInfra hosts 50+ open-source models (Llama 3.3 70B, DeepSeek V3/R1, Qwen2.5) at industry-low pricing from $0.04/M tokens. OpenAI-compatible API with 60-second migration. Serverless batch inference at 50% discount. $1 free credit, no credit card required. Slower than Groq/Cerebras (150 tok/s on 70B) but 5-10x cheaper. No fine-tuning or enterprise SLA. For cost-sensitive batch processing, dataset generation, and research on a budget: best pick.
Introduction: The Cost-First LLM Inference Platform
DeepInfra launched in 2022 with a singular focus: making open-source LLM inference as cheap as possible. While Groq chased speed records and Cerebras built custom silicon, DeepInfra bet on a more conventional but ruthlessly optimized infrastructure stack — high-end NVIDIA GPUs (H100, H200, A100) running a custom inference engine that minimizes token-time costs.
The result is a serverless inference platform that hosts 50+ open-weight models at prices consistently 30-60% below competitors. Llama 3.3 70B runs at $0.35/M input + $0.40/M output — cheaper than Together AI, Fireworks, or self-hosting on AWS. DeepSeek V3 and DeepSeek R1 are available at $0.45/M and $0.55/M respectively, making DeepInfra the most affordable place to access these frontier-tier reasoning models.
For developers, the appeal is straightforward: the same OpenAI client code, a single base_url swap, and your application gets dramatically cheaper inference. DeepInfra speaks the OpenAI API specification natively, including function calling, JSON mode, streaming, and vision (on supported models). The trade-offs are real — throughput per request is slower than Groq or Cerebras, and there's no fine-tuning service — but for cost-sensitive production workloads, batch processing, or research projects running on a budget, DeepInfra is hard to beat.
DeepInfra API Pricing
DeepInfra uses a per-token pay-as-you-go model with separate input and output rates. Pricing is transparent and published per-model in the dashboard. There's no subscription, no minimum commitment, and no hidden infrastructure surcharge.
| Model | Input ($/M tok) | Output ($/M tok) | Context Window | Notes |
|---|---|---|---|---|
| Llama 3.3 70B Instruct | $0.35 | $0.40 | 128K | Meta flagship, best price/quality |
| Meta-Llama-3.1-405B-Instruct | $0.90 | $0.90 | 128K | Frontier-tier open model |
| Meta-Llama-3.1-8B-Instruct | $0.04 | $0.05 | 128K | Cheapest production-grade 8B |
| Meta-Llama-3.1-70B-Instruct | $0.35 | $0.40 | 128K | Legacy 70B |
| Mistral Small 24B | $0.07 | $0.07 | 32K | Cost-effective European model |
| Qwen2.5-72B-Instruct | $0.35 | $0.40 | 128K | Top-tier Chinese model |
| Qwen2.5-Coder-32B | $0.10 | $0.10 | 32K | Best price/quality for code |
| DeepSeek V3 | $0.45 | $0.55 | 64K | Stronger than Llama 70B |
| DeepSeek R1 | $0.55 | $2.19 | 64K | Reasoning model, premium output |
| Phi-4 (14B) | $0.07 | $0.07 | 16K | Microsoft small model |
| Gemma 2 27B | $0.18 | $0.18 | 8K | Google open model |
Free Tier
DeepInfra offers $1 in free credits upon signup, no credit card required. This is enough to run approximately 2.5M tokens of Llama 3.3 70B or 20M tokens of the 8B model — enough for evaluation and small prototypes. After the credit is consumed, you top up with prepaid credits starting at $5.
Batch Inference (50% Off)
DeepInfra's standout cost feature is serverless batch inference: submit up to 1,000 requests in a single batch API call, and DeepInfra processes them at 50% off the per-token price with results delivered within 24 hours. This is ideal for evaluation pipelines, dataset labeling, bulk summarization, or any workload that doesn't need real-time responses.
How Much Can You Get for $100?
At $0.35/M input + $0.40/M output for Llama 3.3 70B, $100 buys approximately 270 million tokens of combined input + output. In practical terms:
- ~13,500 long-form conversations (10K input + 10K output each)
- ~540,000 API calls with 500 tokens each
- ~30 hours of continuous chat at typical 70B speeds
This is roughly 3x the token volume you'd get from the same $100 spent on OpenAI GPT-4o, making DeepInfra the most cost-efficient frontier-tier inference option.
Speed Benchmark: DeepInfra vs. Alternatives
DeepInfra is not the fastest — Groq and Cerebras hold that title. But for cost-adjusted throughput (tokens per dollar per second), DeepInfra is competitive.
| Provider | Llama 3.3 70B Speed | Latency (first token) | Pricing ($/M tok combined) |
|---|---|---|---|
| DeepInfra | ~150 tok/s | under 500ms | $0.375 (blended) |
| Cerebras | 2,000+ tok/s | under 200ms | $0.60 |
| Groq (LPU) | 450 tok/s | under 300ms | $1.78/M (in+out sum) |
| Together AI | 120 tok/s | under 1s | $1.38/M |
| Fireworks AI | 180 tok/s | under 600ms | $1.40/M |
| OpenAI GPT-4o | 80 tok/s | under 500ms | $12.50/M |
DeepInfra is 5-10x slower than Cerebras or Groq but 5-10x cheaper per token. For non-real-time workloads (batch processing, offline document analysis, dataset generation), DeepInfra's cost advantage dominates.
DeepInfra API: OpenAI-Compatible, Drop-in Replacement
The killer feature for adoption is API compatibility. DeepInfra implements the full OpenAI Chat Completions API spec, including:
- Streaming (
stream: true) - Function calling / tool use
- JSON mode (
response_format: { type: "json_object" }) - System messages
- Multi-turn conversations
- Vision inputs (Llama 3.2 Vision, Qwen-VL models)
- Token usage reporting
Migration takes 60 seconds:
# Switch from OpenAI to DeepInfra — only base_url and api_key change
from openai import OpenAI
client = OpenAI(
api_key="YOUR_DEEPINFRA_TOKEN",
base_url="https://api.deepinfra.com/v1/openai",
)
response = client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct",
messages=[{"role": "user", "content": "Explain token pricing in 2 sentences."}],
)
print(response.choices[0].message.content)No SDK changes, no new abstraction layer, no data migration. The same openai-python library, the same request/response shapes, the same error codes.
Model Selection: DeepSeek, Qwen, Mistral, Phi, Llama
DeepInfra's catalog is a developer's best friend — every major open-source model lands within weeks of release. As of June 2026:
Frontier-tier (70B+):
- Llama 3.3 70B Instruct
- Meta-Llama-3.1-405B-Instruct
- Qwen2.5-72B-Instruct
- DeepSeek V3 (671B MoE, 37B active)
- DeepSeek R1 (reasoning model)
Mid-tier (7B-32B):
- Mistral Small 24B, Mistral 7B
- Qwen2.5-Coder-32B-Instruct
- Phi-4 (14B)
- Gemma 2 27B
Small/efficient:
- Llama 3.1 8B
- Mistral 7B
- Phi-3.5 Mini
Specialized:
- DeepSeek Coder V2 (code-specific)
- CodeLlama models
- Whisper (speech-to-text)
- Llama 3.2 Vision (multimodal)
The 50+ model count is roughly 3x what Groq or Cerebras offer, making DeepInfra the most versatile platform for testing across model families.
Use Cases: When to Choose DeepInfra
| Use Case | Recommended? | Why |
|---|---|---|
| Real-time chatbots | ⚠️ Maybe | Cerebras/Groq faster, but DeepInfra fine if cost matters |
| Code completion (Copilot-like) | ✅ Yes | Sub-500ms latency, low cost per token |
| Batch document analysis | ✅ Best | 50% batch discount, large context |
| Dataset generation | ✅ Best | Cheapest frontier-tier 70B for synthetic data |
| Research experiments | ✅ Best | Cheap to run 1000+ model comparisons |
| Production customer-facing LLM | ⚠️ Maybe | No SLA; consider Together AI or Fireworks for SLA |
| Multimodal (vision) | ⚠️ Limited | Vision support exists but not as broad as OpenAI |
| China-direct access | ❌ No | Requires proxy; consider aliyun/zhipu/tencent instead |
DeepInfra vs. Together AI vs. Groq vs. Fireworks
| Dimension | DeepInfra | Together AI | Groq | Fireworks AI |
|---|---|---|---|---|
| Model count | 50+ | 200+ | 7 | 100+ |
| Cheapest 70B | $0.35/M | $0.59/M | $0.79/M | $0.50/M |
| Speed (70B tok/s) | 150 | 120 | 450 | 180 |
| Free tier | $1 credit | $5 credit | Rate-limited free | $1 credit |
| Fine-tuning | ❌ | ✅ | ❌ | ✅ |
| Batch discount | ✅ 50% | ❌ | ❌ | ✅ 30% |
| Enterprise SLA | ❌ | ✅ | ✅ | ✅ |
| OpenAI compatible | ✅ | ✅ | ✅ | ✅ |
Choose DeepInfra when: cost is the primary concern, you need DeepSeek V3/R1 access, you run batch processing, or you're doing research on a budget.
Choose Together AI when: you need fine-tuning, a broader model catalog, or enterprise SLA.
Choose Groq when: raw speed is critical (voice agents, real-time chat, code completion).
Choose Fireworks AI when: you need fine-tuning + fast inference + good enterprise support.
Pros and Cons
Pros:
- ✅ Lowest LLM API pricing in the industry for most models
- ✅ 50+ open-source models including DeepSeek V3/R1, Qwen2.5, Llama 3.3
- ✅ OpenAI-compatible API — 60-second migration
- ✅ 405B model access at $0.90/M (unique pricing tier)
- ✅ Serverless batch inference at 50% discount
- ✅ $1 free credit for evaluation, no credit card
Cons:
- ⚠️ Slower per-request throughput than Groq/Cerebras (150 vs 2,000 tok/s)
- ⚠️ China access requires stable proxy
- ⚠️ No fine-tuning service
- ⚠️ No enterprise SLA — best-effort uptime only
- ⚠️ Limited multimodal support (vision, audio) compared to OpenAI
FAQ
Q: Is DeepInfra actually cheaper than Together AI?
A: Yes, consistently 30-50% cheaper on the same models. Llama 3.3 70B on DeepInfra is $0.35/M input vs Together AI's $0.59/M. DeepSeek V3 is $0.45/M on DeepInfra vs $0.90/M on Together AI. The trade-off is throughput speed and enterprise features.
Q: Can I use DeepInfra from China?
A: Not directly. api.deepinfra.com is frequently blocked by the GFW. Developers in mainland China typically route through a proxy, use Hong Kong/Taiwan endpoints, or aggregate via OpenAI-compatible resellers like FreeModel that handle multiple backends. For fully China-direct access, consider Alibaba Cloud Bailian, Zhipu, or Tencent Hunyuan.
Q: Does DeepInfra support fine-tuning?
A: No. DeepInfra is inference-only. For fine-tuning, use Together AI, Fireworks AI, or run your own training on RunPod / Lambda Labs.
Q: How does the batch inference 50% discount work?
A: Submit up to 1,000 requests in a single API call to the batch endpoint. DeepInfra processes them within 24 hours at 50% off the per-token price. This is ideal for dataset generation, bulk classification, or any workload that doesn't need synchronous responses.
Q: Is DeepInfra production-ready?
A: For most startups and mid-scale applications, yes. The platform has 99.9%+ uptime historically, but no formal SLA. For regulated industries (finance, healthcare) or mission-critical workloads, consider Together AI, Fireworks, or Azure OpenAI for SLA-backed deployments.
Q: Can I run DeepSeek R1 on DeepInfra?
A: Yes. DeepInfra was one of the first providers to host DeepSeek R1 at scale. Pricing is $0.55/M input + $2.19/M output (the output is higher because reasoning chains produce more tokens).
Conclusion: The Budget Choice for Open-Source LLM Inference
DeepInfra is the cost-optimized LLM API for developers who care more about price-per-token than peak throughput. With 50+ models including Llama 3.3 70B at $0.35/M, DeepSeek V3, and Qwen2.5, the platform covers nearly every open-source need at the lowest prices in the industry.
For real-time applications where latency matters, pair DeepInfra with Groq or Cerebras. For cost-sensitive batch processing, dataset generation, or research projects, DeepInfra is the clear winner. The OpenAI-compatible API means migration takes 60 seconds and you can A/B test costs in a single afternoon.
If you're building on a budget and need frontier-tier open-source models, DeepInfra is the starting point. If you need fine-tuning, SLA, or peak speed, graduate to Together AI, Fireworks, or Groq. For China-direct access, use Alibaba Cloud Bailian, Zhipu, or Tencent Hunyuan. For a managed multi-vendor setup with moderation routing that works alongside DeepInfra, sign up at FreeModel.