The AI arena is free today

Open Superagent

GPT-3.5 Turbo vs GPT-6.1 Terra

GPT-6.1 Terra leads the LLM Stats Score 47.5 to -9.8. GPT-3.5 Turbo is 2.7x cheaper per token.

OpenAI · OpenAI · Updated for 2026

Which is better?

GPT-6.1 Terra leads the overall LLM Stats Score 47.5 to -9.8, ranking #36 overall.

On price, GPT-3.5 Turbo is roughly 2.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

GPT-6.1 Terra also accepts a larger context window (1,050,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GPT-3.5 Turbo

  • cost matters — it's about 2.7x cheaper per token

Choose GPT-6.1 Terra

  • overall performance matters — it scores 47.5 and ranks #36 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you process long inputs — it offers a 1,050,000 token context window
  • you want the most recent training data — it shipped Oct 2026

At a glance

The differences that matter most.

GPT-3.5 Turbo
OpenAI
GPT-6.1 Terra
OpenAI
Core performance indexes
LLM Stats Score
-9.8
#401
47.5
#36
Reasoning
-9.1
#394
49.0
#19
Coding
-4.6
#292
37.1
#30
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.50 / M
$1.00 / M
Output price
$1.50 / M
$5.00 / M
Context window
16,385
1,050,000

Individual benchmarks

8 reported for GPT-3.5 Turbo · 8 for GPT-6.1 Terra

No common benchmarks found

GPT-3.5 Turbo and GPT-6.1 Terradon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

GPT-3.5 Turbo costs less

For input processing, GPT-3.5 Turbo ($0.50/1M tokens) is 2.0x cheaper than GPT-6.1 Terra ($1.00/1M tokens).

For output processing, GPT-3.5 Turbo ($1.50/1M tokens) is 3.3x cheaper than GPT-6.1 Terra ($5.00/1M tokens).

In conclusion, GPT-6.1 Terra is more expensive than GPT-3.5 Turbo.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Thu Oct 29 2026 • llm-stats.com
OpenAI
GPT-3.5 Turbo
Input tokens$0.50
Output tokens$1.50
Best providerAzure
OpenAI
GPT-6.1 Terra
Input tokens$1.00
Output tokens$5.00
Best providerOpenAI
Notice missing or incorrect data?

Context Window

Maximum input and output token capacity

GPT-6.1 Terra accepts 1,050,000 input tokens compared to GPT-3.5 Turbo's 16,385 tokens. GPT-6.1 Terra can generate longer responses up to 128,000 tokens, while GPT-3.5 Turbo is limited to 4,096 tokens.

OpenAI
GPT-3.5 Turbo
Input16,385 tokens
Output4,096 tokens
OpenAI
GPT-6.1 Terra
Input1,050,000 tokens
Output128,000 tokens
Thu Oct 29 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

GPT-6.1 Terra supports multimodal inputs, whereas GPT-3.5 Turbo does not.

GPT-6.1 Terra can handle both text and other forms of data like images, making it suitable for multimodal applications.

GPT-3.5 Turbo

Text
Images
Audio
Video

GPT-6.1 Terra

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

GPT-3.5 Turbo

Proprietary

Closed source

GPT-6.1 Terra

Proprietary

Closed source

Release Timeline

When each model was launched

GPT-3.5 Turbo was released on 2023-03-21, while GPT-6.1 Terra was released on 2026-10-26.

GPT-6.1 Terra is 44 months newer than GPT-3.5 Turbo.

GPT-3.5 Turbo

Mar 21, 2023

3.6 years ago

GPT-6.1 Terra

Oct 26, 2026

0 days ago

3.6yr newer

Knowledge Cutoff

When training data ends

GPT-3.5 Turbo has a knowledge cutoff of 2021-09-30, while GPT-6.1 Terra has a cutoff of 2026-06-01.

GPT-6.1 Terra has more recent training data (up to 2026-06-01), making it potentially better informed about events through that date compared to GPT-3.5 Turbo (2021-09-30).

GPT-3.5 Turbo

Sep 2021

GPT-6.1 Terra

Jun 2026

4.8 yr newer

Provider Availability

GPT-3.5 Turbo is available from Azure, OpenAI. GPT-6.1 Terra is available from OpenAI.

GPT-3.5 Turbo

azure logo
Azure
Input Price:Input: $0.50/1MOutput Price:Output: $1.50/1M
openai logo
OpenAI
Input Price:Input: $0.50/1MOutput Price:Output: $1.50/1M

GPT-6.1 Terra

openai logo
OpenAI
Input Price:Input: $1.00/1MOutput Price:Output: $5.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against GPT-3.5 Turbo and GPT-6.1 Terra side-by-side, then vote on the output you prefer.

GPT-3.5 Turbo
✓ Preferred
GPT-6.1 Terra
Open in Playground

FAQ

Common questions about GPT-3.5 Turbo vs GPT-6.1 Terra.

Which is better, GPT-3.5 Turbo or GPT-6.1 Terra?

GPT-6.1 Terra leads the LLM Stats Score 47.5 to -9.8. GPT-3.5 Turbo is made by OpenAI and GPT-6.1 Terra is made by OpenAI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GPT-3.5 Turbo compare to GPT-6.1 Terra in benchmarks?

GPT-3.5 Turbo scores DROP: 70.2%, MMLU: 69.8%, HumanEval: 68.0%, MGSM: 56.3%, MATH: 43.1%. GPT-6.1 Terra scores HealthBench Consensus: 95.1%, HealthBench Professional: 62.7%, Agents' Last Exam: 60.0%, Terminal-Bench-Science 0.1: 58.3%, MentalHealthBench: 55.5%.

Is GPT-3.5 Turbo cheaper than GPT-6.1 Terra?

GPT-3.5 Turbo is 2.0x cheaper for input tokens. GPT-3.5 Turbo costs $0.50/M input and $1.50/M output via azure. GPT-6.1 Terra costs $1.00/M input and $5.00/M output via openai.

What are the context window sizes for GPT-3.5 Turbo and GPT-6.1 Terra?

GPT-3.5 Turbo supports 16K tokens and GPT-6.1 Terra supports 1.1M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GPT-3.5 Turbo and GPT-6.1 Terra?

Key differences include LLM Stats Score (-9.8 vs 47.5), context window (16K vs 1.1M), input pricing ($0.50 vs $1.00/M), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

Related comparisons