deploybase

Together AI vs OpenAI: Price and Performance Comparison

Deploybase · October 1, 2025 · LLM Pricing

Contents

API Pricing Overview

Together AI vs OpenAI represents a core decision in production AI systems. OpenAI offers market-leading models (GPT-4 Turbo, GPT-4o). Together AI provides competitive pricing through open-source models (Llama 3.1, Mistral) and cached tokens. This guide compares actual pricing as of March 2026.

OpenAI's pricing structure:

  • GPT-4o: $2.50/1M input tokens, $10/1M output tokens
  • GPT-4 Turbo: $10/1M input tokens, $30/1M output tokens
  • GPT-3.5 Turbo: $0.50/1M input tokens, $1.50/1M output tokens

Together AI's pricing structure:

  • Llama 3.1 70B: $0.88/1M input tokens, $1.06/1M output tokens
  • Mistral 7B: $0.12/1M input tokens, $0.36/1M output tokens
  • Mixtral 8x22B: $1.08/1M input tokens, $2.70/1M output tokens

See Together AI pricing for complete model list and OpenAI API pricing for latest rates.

Together AI undercuts OpenAI on price by 60-80% for equivalent capability models. A task costing $1.00 on OpenAI costs $0.20-0.30 on Together AI. This advantage compounds on high-volume applications.

The trade-off manifests in model quality. GPT-4o outperforms Llama 3.1 70B on complex reasoning tasks. For straightforward text generation, summarization, and classification, Llama 3.1 70B matches GPT-3.5 Turbo performance.

Token Pricing Deep Dive

Token pricing varies dramatically across models. Input and output token rates differ, incentivizing short inputs and long outputs.

OpenAI GPT-4o: Input $2.50/1M, Output $10/1M

  • Short prompt (100 tokens) + long response (1000 tokens): $0.00000025 + $0.00001 = $0.0000103
  • Same task on Together AI Llama 3.1: $0.000000088 + $0.00000106 = $0.00000115
  • Together AI costs ~9x less

Together AI's rate structure favors long-context use cases. Input tokens cost 20% of output tokens, encouraging prompt reuse and batch processing.

Consider caching. OpenAI offers 90% discount on cached input tokens. Cached tokens cost $0.25/1M on GPT-4o input. This creates strategic advantage for repeated queries against fixed knowledge bases.

Together AI supports prompt caching at $0.088/1M on Llama 3.1 input. The absolute cost runs lower, but the discount percentage matches OpenAI's 90%.

For a system processing 1M tokens daily against stable context (100K context, 5K queries):

  • Without caching: $5.00/day on OpenAI, $0.88/day on Together AI
  • With caching: $0.25 + $15 = $15.25/month on OpenAI, $0.088 + $8.80 = $8.89/month on Together AI

Caching economics shift decision-making. Stable context systems favor API providers over fine-tuned approaches.

Model Quality and Capability Comparison

GPT-4o excels at:

  • Complex reasoning and math
  • Code generation and review
  • Long-chain-of-thought problems
  • Ambiguous instruction interpretation
  • Multi-language performance

Llama 3.1 70B excels at:

  • Fast inference latency
  • Cost-optimized throughput
  • Domain-specific fine-tuning
  • Instruction following
  • Function calling accuracy

Standardized benchmarks show:

  • MMLU (general knowledge): GPT-4o 88%, Llama 3.1 70B 85%
  • GSM8K (math): GPT-4o 92%, Llama 3.1 70B 83%
  • HumanEval (coding): GPT-4o 92%, Llama 3.1 70B 81%
  • MATH (competition math): GPT-4o 68%, Llama 3.1 70B 42%

Gap widens on specialized tasks. GPT-4o demonstrates superior performance on:

  • Multi-step mathematical reasoning
  • Advanced coding architectures
  • Non-English language tasks
  • Creative writing with style transfer

Llama 3.1 70B performs comparably on:

  • Information extraction
  • Text classification
  • Summarization
  • Sentiment analysis

Choose based on workload:

  • Reasoning/coding-heavy: GPT-4o
  • High-volume classification/extraction: Together AI Llama 3.1
  • Budget-constrained sentiment analysis: Together AI

Latency and Throughput Metrics

OpenAI provides SLA guarantees. Request latency typically ranges 500ms-1000ms for GPT-4o. Time-to-first-token averages 200-300ms. Total response time for 1000-token output: 3-5 seconds.

Together AI throughput varies by model:

  • Llama 3.1 70B: 200-400 tokens/second
  • Time-to-first-token: 150-250ms
  • Total response time for 1000-token output: 2.5-5 seconds

Together AI responses arrive faster on average due to distributed infrastructure. OpenAI's centralized systems guarantee consistency. For applications requiring <500ms response time, API solutions struggle regardless of provider.

Throughput scaling differs. OpenAI handles massive concurrent load (10000+ concurrent requests). Together AI handles 1000-5000 concurrent requests depending on model.

For small-scale applications (<100 concurrent users), latency differences prove negligible. For large-scale services (1000+ concurrent users), OpenAI's infrastructure becomes advantageous despite higher cost.

Cost Per Use Case Analysis

Email Classification (1000 emails/day, 200 tokens input average, 50 tokens output)

OpenAI GPT-3.5 Turbo cost:

  • Input: 1000 × 200 × $0.50/1M = $0.10
  • Output: 1000 × 50 × $1.50/1M = $0.075
  • Daily: $0.175, Annual: $63.88

Together AI Mistral 7B cost:

  • Input: 1000 × 200 × $0.12/1M = $0.024
  • Output: 1000 × 50 × $0.36/1M = $0.018
  • Daily: $0.042, Annual: $15.33

Together AI saves 76% annually on email classification.

Code Generation (100 requests/day, 500 token input, 1000 token output)

OpenAI GPT-4o cost:

  • Input: 100 × 500 × $2.50/1M = $0.125
  • Output: 100 × 1000 × $10/1M = $1.00
  • Daily: $1.125, Annual: $410.63

Together AI Llama 3.1 70B cost:

  • Input: 100 × 500 × $0.88/1M = $0.044
  • Output: 100 × 1000 × $1.06/1M = $0.106
  • Daily: $0.15, Annual: $54.75

OpenAI costs ~7.5x more for equivalent capability, but code quality differences matter. GPT-4o generates production-ready code. Llama 3.1 requires more review cycles.

Customer Support Chatbot (10000 interactions/day, 300 token avg input, 200 token avg output)

OpenAI GPT-3.5 Turbo:

  • Input: 10000 × 300 × $0.50/1M = $1.50
  • Output: 10000 × 200 × $1.50/1M = $3.00
  • Daily: $4.50, Annual: $1,642.50

Together AI Mistral 7B:

  • Input: 10000 × 300 × $0.12/1M = $0.36
  • Output: 10000 × 200 × $0.36/1M = $0.72
  • Daily: $1.08, Annual: $394.20

Together AI saves $1,248 annually while maintaining acceptable chatbot quality.

Chatbot economics tip toward Together AI at scale. Customer support conversations require less reasoning than code generation.

See Anthropic API pricing for Claude alternatives.

Infrastructure and Reliability Comparison

OpenAI operates centralized infrastructure with 99.95% uptime SLA. Geographic redundancy provides consistent latency globally.

Together AI uses distributed cloud infrastructure. Availability reaches 99.9% but exhibits geographic variance. Requests from EU regions see increased latency.

Production applications should implement fallback strategies. Consider Together AI as primary provider with OpenAI fallback for mission-critical requests. The cost structure supports this hybrid approach.

Billing transparency differs. OpenAI provides granular usage reports. Together AI reports combined token counts, requiring manual audit trails for cost allocation.

Integration and Ecosystem Factors

OpenAI integrates directly with:

  • Azure OpenAI Service
  • LangChain with first-class support
  • LlamaIndex for RAG applications
  • Popular no-code tools

Together AI integrates with:

  • vLLM for production inference
  • LangChain with community support
  • LlamaIndex with community support
  • Self-hosted deployment options

Development velocity implications appear here. Teams building on OpenAI use extensive documentation and examples. Together AI requires more custom implementation.

For startups with limited engineering resources, OpenAI's ecosystem advantage often justifies higher cost. For well-resourced teams, Together AI's flexibility enables custom optimization.

When to Use Together AI

High-volume, latency-tolerant applications: Batch processing, email analysis, content moderation. Cost savings compound, making Together AI 5-10x cheaper than OpenAI.

Cost-sensitive classification: Email classification, sentiment analysis, topic tagging. Llama 3.1 70B performs at 95%+ GPT-3.5 capability at 15% cost.

Proprietary domain fine-tuning: Together AI enables model fine-tuning. GPT-4o does not support fine-tuning. Fine-tuned Llama 3.1 can achieve task-specific performance exceeding GPT-3.5.

Data privacy and sovereignty: Together AI supports self-hosted deployment. Regulatory requirements (healthcare, finance) favor Together AI.

When to Use OpenAI

Complex reasoning and math: GPT-4o's reasoning capacity exceeds Llama 3.1 significantly. Reserve OpenAI for reasoning-intensive tasks.

Production code generation: GPT-4o generates immediately usable code. Llama 3.1 requires more review and correction cycles.

Multi-language applications: GPT-4o outperforms significantly on non-English languages. Translation and multilingual chat systems favor OpenAI.

Mission-critical reliability: OpenAI's SLA guarantees and centralized infrastructure provide unmatched reliability.

Hybrid Strategy Recommendation

Optimal cost-performance architecture uses both providers:

  • Route straightforward requests (classification, extraction) to Together AI
  • Route complex requests (reasoning, code) to OpenAI
  • Implement intelligent request routing based on complexity estimation
  • Use OpenAI as fallback when Together AI underperforms

Expected cost reduction: 40-50% versus OpenAI-only, with negligible quality degradation.

FAQ

Does Together AI offer production SLAs? Together AI offers 99.9% uptime guarantees on paid production plans. OpenAI's 99.95% SLA provides slightly higher reliability with stronger SLO enforcement.

Can I fine-tune models on Together AI? Yes. Together AI supports fine-tuning on Llama, Mistral, and Mixtral models. OpenAI offers fine-tuning for GPT-3.5 and GPT-4 Mini only (as of March 2026).

How does latency compare for real-time applications? OpenAI: 800ms-1500ms typical. Together AI: 600ms-1200ms typical. Difference negligible for user-facing applications. Both require caching strategies for sub-500ms targets.

Which provider supports longer context windows? Together AI Llama 3.1 supports 128K context. OpenAI GPT-4 Turbo supports 128K context. Both match at maximum context length. Context length rarely becomes the limiting factor.

Can I use Together AI API for commercial applications? Yes. Together AI's commercial license permits production deployment without restrictions. Verify your use case complies with model-specific acceptable use policies.

Should I prepay for tokens to get volume discounts? OpenAI offers no prepaid discounts. Together AI offers no prepaid discounts. Both use pay-as-you-go pricing. No advantage to prepayment on either platform.

How often do prices change? OpenAI typically adjusts pricing quarterly. Together AI adjusts pricing monthly based on infrastructure costs. Budget for 5-10% annual price variation on either platform.

Sources

  • Together AI official pricing documentation (March 2026)
  • OpenAI official pricing documentation
  • OpenAI Model Card and GPT-4o technical report
  • Meta Llama 3.1 technical documentation
  • Standardized benchmark results from Hugging Face Leaderboard