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AI Tool Comparison

Comparing as AI LLM APIs (Foundation Models)
DeepSeek vs Together AI

Compare features, pricing, pros & cons, and user ratings to decide which AI tool is best for your needs.

VS
DeepSeek official logo and brand icon

DeepSeek

Freemium
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Together AI official logo and brand icon

Together AI

Paid
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Verdict by Category

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Feature & Spec Matrix

Detailed Comparison24 Criteria

Complete side-by-side breakdown of specifications, capabilities, and feature parity

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Feature / Spec
DeepSeek
DeepSeek
Together AI
Together AI
Pricing Model
Difference
FreemiumDeepSeek's web chat and apps are free to use, and the open model weights can be downloaded and self-hosted at no license cost. The API is pay-as-you-go with no subscription. Per the official API pricing page, deepseek-flash (V4.1-Flash) costs $0.15 per million input tokens on a cache miss and $0.60 per million output tokens at off-peak rates, rising to $0.30 and $1.20 at peak. deepseek-v4-pro costs $0.66 input and $1.98 output off-peak, and $1.32 and $3.96 at peak. Cache-hit input is far cheaper, from $0.003 to $0.044 per million tokens. Both models offer a 1M-token context window. Peak hours are 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays; all other times are off-peak at half price. Search and Fetch tools cost $0.75 per 1,000 successful requests. The page says calls made through DeepSeek Harness, Claude Code or Codex are free for now, with billing starting November 1, 2026. Prices and model names change often, so check the official pricing page before budgeting.
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.
Starting Price
Difference
$0 Free — API from $0.15/1M tokens
Pay-as-you-go ($0.18–$0.90/1M tokens depending on model size)
Free Plan & Trial Scope
Difference
Free web chat and app; open model weights are free to download and self-host. The API is pay-as-you-go.
$5 in free developer credits upon account creation and GitHub authentication
User Rating Score
Unrated
Unrated
Ideal Workflow / Use Case
Difference
Developers and teams who want low-cost open-weight models for coding agents, reasoning and self-hosted AI
AI application developers building production LLM apps, SaaS startups replacing expensive proprietary models with open-weight models, and enterprise fine-tuning
Enterprise & Scalability
Difference
Self-hostable open weights for private deployment, OpenAI-compatible API, 1M-token context, and cache-based cost reduction for repeated prompts
Together Inference Engine (high-throughput, ultra-low latency serving for Llama 3, DeepSeek-V3, Qwen 2.5, Flux.1, and Whisper), Custom Fine-Tuning API (fine-tune open-source models on your custom datasets in a few clicks), Dedicated GPU Clusters (reserve Nvidia H100 and H200 clusters for large-scale production workloads), Function Calling & Structured JSON Output, Webhook Callbacks, and SOC 2 Type II compliance
Core Categories
Difference
Large Language Models (LLMs)AI Developer APIs & PlatformsAI Coding Assistants
AI Developer APIs & PlatformsLarge Language Models (LLMs)
V4-Pro and V4-Flash mixture-of-experts models with a 1M-token context window
Supported
Supported
R1 reasoning model with step-by-step chain-of-thought
Difference
DeepSeek Advantage
Not listed
Strong coding performance used as a backend by Aider, Cline and similar agents
Difference
DeepSeek Advantage
Not listed
Open weights, reported as MIT-licensed, for local and self-hosted use
Difference
DeepSeek Advantage
Not listed
Pay-as-you-go API with cache-hit discounts and half-price off-peak hours
Difference
DeepSeek Advantage
Not listed
Free web chat, mobile app and desktop app
Difference
DeepSeek Advantage
Not listed
Search and Fetch tools available through the API
Difference
DeepSeek Advantage
Not listed
DeepSeek Harness desktop app for agent workflows
Difference
DeepSeek Advantage
Not listed
Serverless inference API access to 200+ open-source text, vision, image, audio, and video models
Difference
Not listed
Together AI Advantage
Batch inference for cost-effective asynchronous processing at scale
Difference
Not listed
Together AI Advantage
Dedicated model and container inference on single-tenant GPU hardware
Difference
Not listed
Together AI Advantage
On-demand and reserved GPU clusters spanning H100, H200, B200, and GB200/GB300
Difference
Not listed
Together AI Advantage
Fine-tuning via LoRA or full fine-tuning with supervised and DPO methods
Difference
Not listed
Together AI Advantage
Sandbox environments and code interpreter for AI app and agent development
Difference
Not listed
Together AI Advantage
Managed high-performance storage with zero egress fees
Difference
Not listed
Together AI Advantage
Evaluations tooling to measure and compare model quality
Difference
Not listed
Together AI Advantage
OpenAI-compatible API for easy migration from closed-model providers
Difference
Not listed
Together AI Advantage
DeepSeek icon

DeepSeek Pros & Cons

Pros

  • Very low API prices compared with other frontier-class models
  • Open weights that can be self-hosted, fine-tuned or distilled
  • Strong coding and reasoning results, widely used with open-source coding agents
  • 1M-token context on the current V4 models
  • Free web and mobile apps for casual use

Cons

  • Hosted service is operated from China, which can be a data-residency or compliance concern for some organizations
  • The full V4-Pro model is too large to run locally for most people
  • Model names, versions and prices change frequently, so integrations need maintenance
  • Peak-hour pricing and rate limits can affect heavy production workloads
  • Official documentation is lighter than that of larger US platforms
Together AI icon

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
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