Transparent GPT-5 Support in My Clojure ChatGPT Library
🐾 Pyjama, the Ollama/ChatGPT Clojure Wrapper Now Speaks Fluent GPT-5 (and Still Recognizes Cats)
It’s always a good day when your code gets smarter — and an even better day when it can still identify a cute kitten.
With OpenAI rolling out the GPT-5 family (gpt-5, gpt-5-mini, gpt-5-nano), I wanted my Clojure ChatGPT wrapper to transparently support the new models without breaking a sweat.
Calling GPT-5 Is This Simple
Whether you’re sending text or adding vision, the call looks almost the same:
;; Text-only request
(gpt/chatgpt {:model "gpt-5"
:prompt "Write a haiku about summer rain."})
;; Vision request
(gpt/chatgpt {:model "gpt-5"
:prompt "What is shown in this image?"
:image-path "resources/cute_cat.jpg"})
That’s it. Same function, same keys — add :image-path if you want GPT-5 to see what you see.
Optional keys like :streaming or :temperature fit right in without breaking model support.
The Tests: One Cat, Many Brains
We keep the same adorable resources/cute_cat.jpg, and simply loop through each model to make sure the output is stringy, non-empty, and contains either cat or kitten.
(def models ["gpt-5" "gpt-5-mini" "gpt-5-nano"])
(def cat-img "resources/cute_cat.jpg")
(defn ^:private cat? [s]
(boolean (re-find #"(?i)\b(cat|kitten)\b" (str s))))
(deftest gpt5-vision-tests
(doseq [m models]
(testing (str "Vision with " m)
(let [out (ask-vision m)]
(println "\n[" m "] OUTPUT:\n" out)
(is (seq (str out)))
(is (cat? out))))))
The Cat Results — Side-by-Side
Below is the actual output each GPT-5 variant gave when looking at the same picture of a small, fluffy feline.
| Model | Description Output |
|---|---|
| gpt-5 | “A fluffy tabby cat sitting on a wooden floor, looking up with curious eyes.” |
| gpt-5-mini | “A cute kitten with soft fur and big eyes staring directly at the camera.” |
| gpt-5-nano | “A small cat, likely a kitten, sitting indoors and gazing upwards.” |
(Your exact outputs may vary — models sometimes change phrasing on different runs.)
Why This Matters
By abstracting the model name into the test list, I can:
- Swap in new models with zero boilerplate.
- Keep vision tests consistent across the fleet.
- Verify that newer models behave at least as well as the older ones in common tasks.
What’s Next?
- Multi-image input tests: Will GPT-5 mini get overwhelmed by two kittens?
- Benchmarking latency vs accuracy across the
miniandnanovariants. - Contextual chaining: feeding GPT-5’s own cat descriptions back in for creative remixes (“Describe this cat as if it’s a noir detective”).
Transparent model support isn’t just maintenance — it’s an invitation to experiment. The models evolve fast; our libraries should be just as nimble.