GPT-5 nano

I’ve been curious whether current AI models have any natural aptitude for trading on realtime, raw financial data, without any elaborate news pipelines or convoluted system prompts. I mean literally just raw livestreamed market numbers and a calculator. 

So I built a crypto daytrading arena. All agents consume a realtime stream of ticker data and candlesticks for **BTC**, **SOL**, and **FARTCOIN**. They have access to a calculator and can view their portfolio and holdings. As data flows in, the agent autonomously decides to enter or exit, whenever they want, no guardrails.

I started with four agents, each with $100k in its account: **gpt 5 nano (low reasoning)**, **minimax m2.5**, **grok 4.1 fast (no reasoning)**, and **gemini 2.5 flash**.

After a little more than 24 hrs of continuous trading, here’s roughly where they stand:

* gpt 5 nano: **+$11,500**
* minimax m2.5: +$4000
* gemini 2.5 flash: +$1900
* grok 4.1 fast: -$100

Here's a view of the arena dashboard I've been using to track agent portfolios:

[agent arena dashboard](https://reddit.com/link/1reww2d/video/352q5gcwtqlg1/player)

I'm honestly impressed with how gpt-5-nano has performed so far, considering it's a relatively cheap model. When I started this I definitely wasn't even expecting it to be in the positives by now. It might just be really good at processing raw financial numbers(idk)? I’m keeping these agents running so we’ll see if these gains stay consistent. Eventually I also want to throw in more expensive models (gpt 5.2, sonnet 4.6) and see how they compete too. 

Also, this project is fully open source so you can check it out and run it yourself! [https://github.com/ryan-yuuu/crypto-trading-arena](https://github.com/ryan-yuuu/crypto-trading-arena) 

edit: Thanks for the award! This is my first one!

**Update 2/28:**

I had built some more metrics and trade recording features in order provide data for insights into the arena performance over time. Because of this, I had redeployed the arena on 2/26 \~9pm PT, this time only with gpt5 nano and gemini 2.5 flash. Here are some key highlights since the agents were redeployed \~48hrs ago.

During this session, 2/26 9:19pm PT to 2/28 9:29pm PT:

* Market was bearish. During the session BTC had dipped to -7%, SOL -12%, FART -18%
* Both agents outperformed equal weight buy and hold BITCOIN and SOLANA--let's say this is the "benchmark" for comparison purposes
* gpt-5 nano returned +1.50%, beating the benchmark by +1.90%. It stayed above the benchmark for the entirety of the session
* gemini 2.5 flash was very cash heavy and returned -0.35% but still beat the benchmark by +0.05%. At some point it did dip below the benchmark briefly (by at most -0.05%)

Here are reports I generated to visualize the arena's performance during the 48hr trading session.

* Google drive link to PDF in case the reports don't render in the post: [https://drive.google.com/file/d/1iICEoMPU7JUKUHkjw\_tq5qYp9MeUxr0F/view?usp=sharing](https://drive.google.com/file/d/1iICEoMPU7JUKUHkjw_tq5qYp9MeUxr0F/view?usp=sharing)

[gpt 5 nano and gemini 2.5 portfolio performance](https://preview.redd.it/2yg4ydeatdmg1.png?width=3943&format=png&auto=webp&s=995c01aee42834512cad7cc1d8873d6cd58b9813)

[gpt 5 nano and gemini 2.5 flash perf vs market](https://preview.redd.it/zfktgeeatdmg1.png?width=3927&format=png&auto=webp&s=5b104ba3c64d014541916786270a9d9acacb0f80)

p.s. these redeployed agents are still running so I can provide more updates later on.

GPT5-nano is doing an excellent job with assist. Seems to have no issues handling a long string of different commands. Speed is good and so far my testing has been great!!

https://youtu.be/o30ymyQqQo4?si=p7h95KIKICWIq9IG

Edit: Here’s some details ony setup

Using the stock OpenAI conversion integration with GPT5-nano
STT is Home Assistant Cloud
TTS is Eleven labs
Cloned the Jarvis voice from sound bites I found in an obscure Reddit post with a Google Drive link to said files from an old phone app.
Device is Voice PE taken apart and put in a 3d printed arch reactor case. (Just had to take front plant off.

Hi everyone,

We ran task benchmarks on the GPT-5 series models, and as per general consensus, they are likely not a break through in intelligence. But they are a good replacement of o3, o1 and gpt-4.1. And lower latency and the cost improvements are impressive! Likely really good models for chatgpt, even though users have to get used to them.

**For builders, perhaps one way to look at it:**

o3 and gpt-4.1 -> gpt-5

o1 -> gpt-5-mini

o1-mini -> gpt-5-nano

**But let's look at a tricky failure case to be aware of.**

Part of our context oriented task evals, we task the model to read a travel journal and count the number of visited cities:

Question: "How many cities does the author mention"

Expected: 19

GPT-5: 12

Models that consistently gets this right is gemini-2.5-flash, gemini-2.5-pro, claude-sonnet-4, claude-opus-4, claude-sonnet-3.7, claude-3.5-sonnet, gpt-oss-120b, grok-4.

To be a good model for building with, context attention is one of the primary criterias. What makes Anthropic models stand out is how well they have been utilising the context window even since sonnet-3.5. Gemini series and Grok seems to be putting attention to this as well.

You can read more about our task categories and eval methods here: [https://opper.ai/models](https://opper.ai/models)

For those building with it, anyone else seeing similar strengths/weaknesses?

I’ve been curious whether current AI models have any natural aptitude for trading on realtime, raw financial data, without any elaborate news pipelines or convoluted system prompts. I mean literally just raw livestreamed market numbers and a calculator. 

So I built a crypto daytrading arena. All agents consume a realtime stream of ticker data and candlesticks for **BTC**, **SOL**, and **FARTCOIN**. They have access to a calculator and can view their portfolio and holdings. As data flows in, the agent autonomously decides to enter or exit, whenever they want, no guardrails.

I started with four agents, each with $100k to start: gpt 5 nano (low reasoning), minimax m2.5, grok 4.1 fast (no reasoning), and gemini 2.5 flash.

After a little more than 24 hrs of continuous trading, here’s roughly where they stand:

* gpt 5 nano: **+$11,500**
* minimax m2.5: +$4000
* gemini 2.5 flash: +$1900
* grok 4.1 fast: -$100

I'm honestly impressed with how gpt5-nano has performed so far, considering it's a relatively cheap model. When I started this I definitely wasn't even expecting it to be in the positives by now. It might just be really good at processing raw financial numbers(idk)? I’m keeping these agents running so we’ll see if these gains stay consistent. Eventually I also want to throw in more expensive models (gpt 5.2, sonnet 4.6) and see how they compete too. 

Also, this is fully open source: will provide github repo in comments.

**tldr:** gpt-5-nano, good with money??

https://reddit.com/link/1svgw4o/video/zqh4ydm46dxg1/player

No tricks, no copy-paste. Two completely different AI models, separate conversations - one remembers what the other was told.

The way it works: every message gets embedded and stored. When you open a new chat with any model, your memory is injected into context automatically. GPT, Claude, Gemini, Grok and DeepSeek - they all share the same memory layer.

So when I told GPT-5 Nano "I live in Bahrain" and then opened a fresh Claude Sonnet 4.6 conversation and asked "where do I live?" - it said "Based on your memory, you live in Bahrain 🇧🇭"

Live on [asksary.com](http://asksary.com) now