GPT-3.5 Turbo model profile
Evidence snapshot:
GPT-3.5 Turbo is a OpenAI model profile with a 16,385-token context window. The dated catalog snapshot records text modalities and interfaces for structured output, text generation, tool calling.
GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks. Training data up to Sep 2021.
GPT-3.5 Turbo is a reference profile: OpenAI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
GPT-3.5 Turbo is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.
GPT-3.5 Turbo accepts text and returns text. The snapshot records structured output, text generation, and tool calling as capabilities or interfaces.
The dated reference snapshot lists $0.5 input and $1.5 output per million tokens.
The 2026-09-06 snapshot includes 1 Artificial Analysis index. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.
Profile facts
- Profile type
- Model research profile
- Provider or publisher
- OpenAI
- Context window
- 16,385 tokens
- Snapshot date
- 2026-09-06
- Knowledge cutoff
- 2021-09-30
- Reference catalog ID
- openai/gpt-3.5-turbo
When to pick GPT-3.5 Turbo
GPT-3.5 Turbo is a reference profile: OpenAI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.
Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.
- Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
- Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
- Look elsewhere when the prompt is long. The 16K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route.
Context, modalities, and identity
GPT-3.5 Turbo accepts text and returns text. The snapshot records structured output, text generation, and tool calling as capabilities or interfaces.
- Provider or publisher
- OpenAI
- Context window
- 16,385 tokens
- Maximum output
- 4,096 tokens
- Input modalities
- text
- Output modalities
- text
- Knowledge cutoff
- 2021-09-30
Model overview
GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks. Training data up to Sep 2021.
- Reference model ID
- openai/gpt-3.5-turbo
- Canonical version
- openai/gpt-3.5-turbo
- Hugging Face ID
- Not separately documented
Capabilities and supported controls
Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.
- Supported parameters
- frequency_penalty, logit_bias, logprobs, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
- structured output
- text generation
- tool calling
Dated pricing snapshot
The dated reference snapshot lists $0.5 input and $1.5 output per million tokens.
These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.
- Price date
- 2026-09-06
- Input
- $0.5 per 1M tokens
- Cached input
- No verified rate
- Output
- $1.5 per 1M tokens
Provider routes, performance, and uptime
The snapshot retains 1 provider endpoint. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.
- Active provider endpoints
- 1
- Best p50 latency
- No recent metric
- Best p50 throughput
- No recent metric
- Availability with routing
- No recent metric
- Availability without routing
- No recent metric
- Performance date
- 2026-09-06
| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
|---|---|---|---|---|---|---|---|---|
| OpenAI | unknown | $0.5 / 1M | $1.5 / 1M | No verified rate | 16,385 tokens | No recent p50 | No recent p50 | 100.00% |
Benchmark evidence and limitations
The 2026-09-06 snapshot includes 1 Artificial Analysis index. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.
Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
- Coding Index
- 10.7
Popularity and market context
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.
- Third-party catalog rank
- No verified rank
- Global market share
- Not inferred
- Observation date
- 2026-09-06
Frequently asked questions
When should I use GPT-3.5 Turbo?
GPT-3.5 Turbo is a reference profile: OpenAI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Look elsewhere when you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr. Look elsewhere when the prompt is long: The 16K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route.
What is GPT-3.5 Turbo?
GPT-3.5 Turbo is a OpenAI model profile with a 16,385-token context window. The dated catalog snapshot records text modalities and interfaces for structured output, text generation, tool calling.
What context window does GPT-3.5 Turbo have?
The dated profile lists 16,385 tokens of context and up to 4,096 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.
What benchmark evidence is available for GPT-3.5 Turbo?
The 2026-09-06 snapshot includes 1 Artificial Analysis index. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.
Is GPT-3.5 Turbo available through Kendr?
This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.
Does the popularity rank represent GPT-3.5 Turbo's global market share?
No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated. No global market-share percentage is inferred when the source does not publish one.
Sources and evidence dates
- GPT-3.5 Turbo third-party catalog record (catalog, checked 2026-09-06)
- Third-party model catalog methodology (methodology, checked 2026-09-06)
- OpenAI official model documentation (primary, checked 2026-09-06)
- Artificial Analysis capability indices methodology (benchmark, checked 2026-09-06)