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DeepInfra vs Cohere
DeepInfra is the cheapest place to run 150+ open models. Cohere costs more but adds retrieval models, fine-tuning and on-prem deployment.
By The Subconscious Team · Updated
DeepInfra vs Cohere: key differences
On price, DeepInfra is the reference point. Llama 3.1 8B costs $0.02 per million and DeepSeek V4 Flash $0.14 in and $0.28 out, with no minimums or contracts across 150+ open models. Cohere's cheapest generator, Command R7B, is $0.0375 in, and Command A lists at $2.50 in and $10 out, while Command A+ prices are unpublished. DeepInfra's catch is quantization: its FP4 DeepSeek V4 Pro caps context at 66K, and some reviewers report weaker output unless they pin FP8. Cohere's Command A offers a 256K window, and Cohere says Command A+ quantizes to 4-bit with a claimed 375 tokens per second.
Customization and deployment split them further. DeepInfra has no managed fine-tuning and runs only as a shared API. Cohere offers enterprise fine-tuning, including inside private VPC and on-prem deployments, plus Model Vault dedicated instances and availability on Bedrock, Azure AI Foundry and OCI. Embed 4 and Rerank 4 give Cohere a mature retrieval stack. For bulk tagging, extraction or synthetic data where cost per token is everything, DeepInfra wins. For regulated enterprise search where control and support matter more than price, Cohere does.
What DeepInfra and Cohere do
DeepInfra
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 profileCohere
Cohere is a Toronto-based lab that sells models and platforms to banks, governments and large enterprises rather than consumers. Its generative line is the Command family. Command A+, released May 20, 2026, is a 218B-parameter mixture-of-experts model with 25B active, published under Apache 2.0 with a 128K context window, and it combines reasoning, vision, translation and tool use in one set of weights. Command A has a 256K window and lists at $2.50 in and $10 out per million tokens, while Command R7B costs $0.0375 in. June 2026 added North Mini Code, a 30B Apache 2.0 coding model, and the lineup also includes Aya multilingual models and Transcribe for speech.
Example models: Command A+, Command A, Embed 4, Rerank 4
Full Cohere profileShould you choose DeepInfra or Cohere?
DeepInfra vs Cohere at a glance
| Attribute | DeepInfra | Cohere |
|---|---|---|
| Model access | Open weights | Closed, plus open Command A+ |
| Flagship models | DeepSeek V4 Flash, Llama 3.1 8B | Command A+, Command A, Embed 4, Rerank 4 |
| Speed | ~33 tok/s on DeepSeek V4 Pro (FP4) | 375 tok/s on Command A+ W4A4, per Cohere |
| Price | From $0.02 per 1M | $0.0375–$2.50 in, $0.15–$10 out per 1M |
| Customization | No managed fine-tuning | Enterprise fine-tuning, incl. private |
| Deployment | Shared API, no contracts | API, Bedrock, Azure, OCI, VPC, on-prem |
| Long context | 66K on FP4 DeepSeek V4 Pro | 256K on Command A; 128K on A+ |
Frequently asked questions
What is the difference between DeepInfra and Cohere?
DeepInfra is the cheapest place to run 150+ open models. Cohere costs more but adds retrieval models, fine-tuning and on-prem deployment.
When should I choose DeepInfra over Cohere?
Lowest per-token cost on open models; Bulk extraction and synthetic data; Trying many models with no contract.
When should I choose Cohere over DeepInfra?
Enterprise RAG with support and SLAs; Fine-tuning in a private environment; Multimodal embeddings for PDFs and images.
Is DeepInfra or Cohere cheaper?
DeepInfra: From $0.02 per 1M. Cohere: $0.0375–$2.50 in, $0.15–$10 out per 1M. The cheaper choice depends on the model and workload.
Which has more context, DeepInfra or Cohere?
DeepInfra: 66K on FP4 DeepSeek V4 Pro. Cohere: 256K on Command A; 128K on A+.
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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.