DeepSeek-V2.5 vs GPT-3.5 Turbo
DeepSeek-V2.5 leads the LLM Stats Score 8.1 to -9.4. DeepSeek-V2.5 is 4.3x cheaper per token.
DeepSeek · OpenAI · Updated for 2026
Which is better?
DeepSeek-V2.5 leads the overall LLM Stats Score 8.1 to -9.4, ranking #287 overall.
In the 3 individual benchmarks reported for both models, DeepSeek-V2.5 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V2.5 is roughly 4.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GPT-3.5 Turbo also accepts a larger context window (16,385 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 DeepSeek-V2.5
- overall performance matters — it scores 8.1 and ranks #287 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- cost matters — it's about 4.3x cheaper per token
- you want the most recent training data — it shipped May 2024
- you need open weights you can self-host or fine-tune
Choose GPT-3.5 Turbo
- you process long inputs — it offers a 16,385 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for DeepSeek-V2.5 · 8 for GPT-3.5 Turbo
DeepSeek-V2.5 outperforms in 3 benchmarks (HumanEval, MATH, MMLU), while GPT-3.5 Turbo is better at 0 benchmarks.
DeepSeek-V2.5 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 3.6x cheaper than GPT-3.5 Turbo ($0.50/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 5.4x cheaper than GPT-3.5 Turbo ($1.50/1M tokens).
In conclusion, GPT-3.5 Turbo is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
GPT-3.5 Turbo accepts 16,385 input tokens compared to DeepSeek-V2.5's 8,192 tokens. DeepSeek-V2.5 can generate longer responses up to 8,192 tokens, while GPT-3.5 Turbo is limited to 4,096 tokens.
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while GPT-3.5 Turbo uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while GPT-3.5 Turbo was released on 2023-03-21.
DeepSeek-V2.5 is 14 months newer than GPT-3.5 Turbo.
May 8, 2024
2.4 years ago
1.1yr newerMar 21, 2023
3.5 years ago
Knowledge Cutoff
When training data ends
GPT-3.5 Turbo has a documented knowledge cutoff of 2021-09-30, while DeepSeek-V2.5's cutoff date is not specified.
We can confirm GPT-3.5 Turbo's training data extends to 2021-09-30, but cannot make a direct comparison without DeepSeek-V2.5's cutoff date.
—
Sep 2021
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. GPT-3.5 Turbo is available from Azure, OpenAI.
DeepSeek-V2.5
GPT-3.5 Turbo
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and GPT-3.5 Turbo side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs GPT-3.5 Turbo.