A once-useful workhorse now completely overshadowed by cheaper, more capable successors.
42
Coding
48
Writing
30
Research
0
Images
62
Value
5
Long Context
Use this when
High-volume, cost-sensitive text tasks like classification, summarization, and simple Q&A where bleeding-edge quality is not required.
Skip this if
You need to process documents longer than a few paragraphs, require strong reasoning, or are starting a new project where GPT-4o mini or Claude Haiku are viable alternatives.
Low cost at $1/$2 per million tokens makes it viable for large-scale batch processing
Fast inference speed suitable for latency-sensitive applications
Reliable instruction-following for structured, well-defined prompts
Stable, versioned snapshot ensures consistent, reproducible outputs over time
Weaknesses
Severely limited 4,095-token context window makes it unsuitable for long documents or multi-turn conversations
Substantially outclassed by GPT-4o mini at a similar price point for nearly every task
Frozen older checkpoint means it lacks improvements in reasoning, refusals, and factual accuracy from later model versions
Monthly cost estimate
See what OpenAI: GPT-3.5 Turbo (older v0613) actually costs at your usage level
Input tokens / month1M
10k50M
Output tokens / month500k
10k25M
Input cost
$1.00
Output cost
$1.00
Total / month
$2.00
Based on OpenAI: GPT-3.5 Turbo (older v0613) API pricing: $1/1M input · $2/1M output. Real costs vary by provider discounts and caching. Check the provider for exact current rates.
Price History
OpenAI: GPT-3.5 Turbo (older v0613) pricing over time
→0% since Mar 27
2 data points · tracked daily since Mar 27, 2026
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Start using OpenAI: GPT-3.5 Turbo (older v0613)
High-volume, cost-sensitive text tasks like classification, summarization, and simple Q&A where bleeding-edge quality is not required.. Start free — no card required.
Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
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OpenAI: GPT-5 Mini
GPT-5 Mini is OpenAI's budget-tier distillation of GPT-5, designed for high-volume, cost-sensitive tasks that don't require full flagship reasoning depth. It supersedes GPT-4o with improved instruction following and a massively expanded 400K context window at a fraction of the cost.
Verdict
The new budget default for OpenAI API users: faster, cheaper, and smarter than GPT-4o with a context window that punches well above its price tier.
Quality score
66%
Pricing
$0.25/1M in
$2.00/1M out
Speed
Very fast
Best for high-volume production workloads — chatbots, summarization pipelines, and document q&a — where cost efficiency matters more than peak reasoning.
Context
400k tokens
Output cost of $2/1M tokens is higher than some competing budget models (Gemini Flash at ~$0.60/1M output). At scale, output-heavy tasks may erode cost advantages — monitor token ratios carefully. Supersedes GPT-4o, which may be deprecated on a rolling basis.
BudgetFastLong ContextHigh VolumeOpenAI
Best for
High-volume production workloads — chatbots, summarization pipelines, and document Q&A — where cost efficiency matters more than peak reasoning.
Claude 3.5 Haiku is Anthropic's fastest and most affordable model in the Claude 3.5 family, designed for high-throughput tasks requiring quick responses without sacrificing Claude's core instruction-following quality. It handles a massive 200K context window while maintaining speed suitable for production pipelines.
Verdict
The fastest way to get Claude's quality in production — just don't confuse 'fast' with 'cheap'.
Quality score
64%
Pricing
$0.80/1M in
$4.00/1M out
Speed
Very fast
Best for high-volume, latency-sensitive applications like chatbots, classification, data extraction, and agentic tool use where speed and cost matter more than peak reasoning depth.
Context
200k tokens
Output cost of $4/1M is notably higher than competing fast/mini models. Input cost at ~$0.80/1M is competitive. Best value emerges in input-heavy pipelines like document classification or RAG retrieval where output tokens are minimal.
High-volume, latency-sensitive applications like chatbots, classification, data extraction, and agentic tool use where speed and cost matter more than peak reasoning depth.
Gemma 2 9B is Google's open-weight 9-billion parameter model designed for efficient on-device and API deployment. It punches above its weight class for instruction-following and general language tasks at an exceptionally low cost.
Verdict
A capable open-weight budget model hamstrung by a frustratingly small context window.
Quality score
45%
Pricing
$0.03/1M in
$0.09/1M out
Speed
Very fast
Best for lightweight text tasks, classification, and summarization where cost matters more than frontier-level quality.
Context
8k tokens
Pricing reflects API access through third-party providers; Google also offers Gemma 2 9B weights for free download and self-hosting. The 8,192 token limit is a hard architectural constraint of this version.
Open WeightBudgetSmall ModelGoogleOn-Device
Best for
Lightweight text tasks, classification, and summarization where cost matters more than frontier-level quality.
What is OpenAI: GPT-3.5 Turbo (older v0613) best for?
OpenAI: GPT-3.5 Turbo (older v0613) is best for high-volume, cost-sensitive text tasks like classification, summarization, and simple q&a where bleeding-edge quality is not required.. It is a strong fit when that workflow matters more than the tradeoffs around balanced pricing and very fast speed.
When should I avoid OpenAI: GPT-3.5 Turbo (older v0613)?
You need to process documents longer than a few paragraphs, require strong reasoning, or are starting a new project where GPT-4o mini or Claude Haiku are viable alternatives.
What is a cheaper alternative to OpenAI: GPT-3.5 Turbo (older v0613)?
Meta: Llama 3.1 8B Instruct is the lower-cost option to compare first when you want a similar workflow fit with less token spend.
What is a faster alternative to OpenAI: GPT-3.5 Turbo (older v0613)?
OpenAI: GPT-5 Mini is the better pick when response time matters more than maximum depth or premium quality.
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