AI Tool Comparison
Comparing as AI Agent & Orchestration FrameworksOpenAI API vs Together AI
OpenAI API
VS
Together AI
Verdict by Category
Detailed Comparison
Feature
OpenAI API
Together AI
Pricing
PaidThe OpenAI API uses pay-as-you-go, per-token pricing that varies by model. GPT-5.6 Sol, built for complex reasoning and coding, costs $5.00 per 1M input tokens and $30.00 per 1M output tokens with a 1.05M context length. GPT-5.6 Terra, balancing intelligence and cost, costs $2.00 per 1M input tokens and $12.00 per 1M output tokens. GPT-5.6 Luna, designed for cost-sensitive, high-volume workloads, costs $0.20 per 1M input tokens and $1.20 per 1M output tokens. All three share a 1.05M context length and 128K max output tokens. Additional costs apply for fine-tuning, evals, and specialized tools like web search or file search depending on usage. New accounts must add billing details before making live API calls, and there is no free-tier token quota; enterprise organizations can contact sales for custom pricing, dedicated support, and advanced data residency and retention controls.
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 Assistants
AI Developer APIs & Platforms
Summary
Developer platform for GPT models, AI agents, and real-time voice
Full-stack AI cloud for inference, fine-tuning, and GPU clusters
OpenAI API Pros & Cons
Pros
- Access to frontier GPT-5.6 models spanning a full range of intelligence and cost tiers
- Comprehensive platform covering text, agents, voice, and multimodal use cases in one place
- Agents SDK and built-in tools simplify building production-grade autonomous agents
- Strong enterprise security posture, including SOC 2 Type 2 and HIPAA BAAs
- No training on API business data by default, with zero data retention available by request
- Extensive documentation, cookbook examples, and an active developer community
Cons
- Pay-as-you-go token costs can scale quickly for high-volume or long-context applications
- New accounts must add billing details before making API calls, with no ongoing free-tier quota
- Frontier reasoning models like GPT-5.6 Sol carry premium per-token pricing versus smaller models
- Enterprise features like dedicated support and advanced data residency require contacting sales
- Rate limits and model access can vary by usage tier, requiring spend history to unlock higher limits
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