AI Tool Comparison
Comparing as AI LLM APIs (Foundation Models)Mistral AI vs Together AI
Mistral AI
VS
Together AI
Verdict by Category
Detailed Comparison
Feature
Mistral AI
Together AI
Pricing
FreemiumVibe Free offers limited access to Mistral's SOTA models, web and mobile access, limited messages, web searches, and coding sessions, image generation, and 100+ connectors. Vibe Pro is $14.99/month with more messages, web searches, more complex task handling, all-day coding in the CLI, IDE, and web, more image generations, and chat and email support. Team is $24.99/user/month, adding up to 30GB of storage per user, domain name verification, and data export. Enterprise offers custom models, custom agents, custom workflows, audit logs, SAML SSO, and white-labeling via a private deployment, priced on request. A Student plan offers Vibe Pro for $5.99/month for verified students. Separately, the API is billed per million tokens: for example Mistral Large 3 costs $0.5 input / $1.5 output, Medium 3.5 costs $1.5 input / $7.5 output, and Small 4 costs $0.15 input / $0.6 output, with batch processing available at a 50% discount and Enterprise APIs available at a 75% premium for regional controls, SLAs, and premium support.
PaidTogether AI uses pay-as-you-go pricing across its products. Serverless inference is billed per model, priced per 1M tokens for text (e.g., MiniMax M3 at $0.30 input/$1.20 output, GLM-5.2 at $1.40 input/$4.40 output, gpt-oss-120B at $0.15 input/$0.60 output), per image for image generation (e.g., FLUX.1 [schnell] at $0.0027/image), per video for video models (e.g., ByteDance Seedance 2.5 at $0.115/video, Google Veo 3.0 at $1.60/video), and per audio minute or character for speech models. Dedicated Inference runs on single-tenant GPUs starting at $5.49/GPU/hour on-demand for NVIDIA HGX H100 and $8.99/hour for HGX B200, with reserved options available via sales. GPU Clusters offer on-demand rates from $3.99/hour (H100) to $8.19/hour (B200), with reserved pricing dropping as low as $3.19/hour for 181+ day H100 commitments. Sandbox compute costs $0.0446/vCPU/hour and $0.0149/GiB RAM/hour, with Code Interpreter sessions at $0.03 per 60-minute session. Fine-tuning is priced per 1M tokens processed, ranging from $0.48 (LoRA, up to 16B parameters) to $8.00 (full fine-tuning, 70-100B parameters) for standard models, with specialized model pricing (e.g., DeepSeek-R1, GLM-5) ranging $5-$40 per 1M tokens plus a minimum job charge. Managed Storage costs $0.16/GiB/month.
Categories
AI Developer APIs & PlatformsAI Coding AssistantsLarge Language Models (LLMs)
AI Developer APIs & PlatformsLarge Language Models (LLMs)
Summary
Frontier open-weight AI models and the Vibe agent for work and code
Full-stack AI cloud for inference, fine-tuning, and GPU clusters
Mistral AI Pros & Cons
Pros
- Owns its full model stack end-to-end rather than reselling third-party models
- Open-weights even flagship models, giving businesses genuine self-hosting flexibility
- EU-based hosting and self-hosted options are a strong fit for data-sovereignty-sensitive customers
- Vibe unifies chat, work automation, and coding into one agent and one subscription
- Competitive API pricing, especially on cost-efficient models like Small 4 and Ministral
Cons
- Recent rebrand from Le Chat to Vibe (May 2026) may cause confusion for existing users and search results still reference the old name
- API pricing spans 32+ models with different rates per capability, requiring careful reading to estimate true costs at scale
- Commercial self-hosted deployment of open-weight models requires a separate Mistral license beyond the Apache 2.0 research terms
- Smaller ecosystem and community size compared to OpenAI or Anthropic, despite strong open-weight momentum
- Enterprise APIs carry a 75% premium over list pricing on select models for regional data controls and premium support
Together AI Pros & Cons
Pros
- OpenAI-compatible API makes migrating from closed-model providers straightforward
- Transparent per-model, pay-as-you-go pricing across 200+ open-source models
- Vertically integrated GPU cloud offers competitive on-demand and reserved rates
- Backed by deep systems research, including FlashAttention and other efficiency breakthroughs
- Full-stack coverage from inference to fine-tuning to raw GPU compute in one platform
- Proven at scale with customers like Cursor, Zoom, Quora, and ElevenLabs
Cons
- Pricing spans many separate model and product pages, making total cost estimation more complex than flat-rate competitors
- Dedicated GPU and reserved cluster pricing largely requires contacting sales rather than transparent self-serve rates
- Focus on open-source models means access to closed frontier models like GPT or Claude isn't the platform's core strength
- Fine-tuning costs vary significantly by model size and technique, requiring careful comparison before committing
- Provisioned throughput and PTU-based pricing has a learning curve for teams new to capacity-based billing