6 Best ChatGPT Alternatives for Coding

ChatGPT is very popular for coding help, but some developers seek even more advanced or reliable models.

These alternatives are ranked by the Coding category in the aiToggler LLM Leaderboard, which reflects over 2 million community votes.

Each model below is noted for strong coding performance, and all can be tried at aiToggler’s app.

1. Gemini 2.5 Pro (Preview May 2025)

Gemini 2.5 Pro is Google’s newest multipurpose model, tuned for complex reasoning and coding. Google notes it “excels at coding and complex reasoning tasks”.

It even tops community leaderboards: one report calls it “the top model on LM Arena” and “a superb model for… long-context coding.”

In practice it handles long code prompts very well.

This model is available via Google AI Studio (Vertex AI). Its paid API pricing is around $1.25 per 1M input tokens and $10 per 1M output tokens (for prompts up to 200K tokens).

Because it uses “invisible” reasoning tokens, even a short answer can count many tokens. You can test Gemini 2.5 Pro on aiToggler by selecting the model gemini-2.5-pro-preview.

2. Grok 3

Grok 3 is xAI’s (Elon Musk’s AI) advanced model, released in early 2024.

It uses a large-scale reasoning approach and was trained to spend extra compute on hard problems. xAI reports that Grok 3 made big gains: for example, it achieved 79.4% accuracy on a coding benchmark (LiveCodeBench).

Early testers have praised its coding abilities and long reasoning. In community rankings, a preview of Grok 3 (Feb. 2024) reached top scores in the coding category.

Unlimited Grok 3 is available through X’s Premium+ service, which is $50/month. You can also select grok-3 on aiToggler to try this model’s coding capabilities.

3. LLaMA 4 Maverick

LLaMA 4 Maverick is Meta’s new mixture-of-experts model (17B parameters with 128 experts).

Meta markets it as a highly efficient model that can beat other top models on coding and reasoning. Its architecture is built for large efficiency. However, independent tests give mixed results. A benchmark by Rootly AI found LLaMA 4 Maverick scored only 69.5% accuracy on a coding bug-fix test – below specialized coding models like DeepSeek and Qwen2.5.

So while it may be very fast and multimodal, it was not the top coder in that test. LLaMA 4 Maverick is not a consumer service, so there is no subscription price – it is released by Meta for research and in private trials.

On aiToggler, you can try free and paid version by selecting it in the list.

4. Gemini 2.5 Flash (Preview April 2025)

Gemini 2.5 Flash is a new Google model designed for cost-efficiency with a very large context window.

Google calls it their “first hybrid reasoning model” with a 1,000,000-token context.

It uses a combination of fast and high-quality reasoning (“Flash” vs “Pro”). Because of this design, Gemini Flash can handle huge codebases in one prompt.

Google’s pricing reflects its efficiency: the paid tier charges only about $0.60 per 1M output tokens in the non-thinking mode (and $3.50 in thinking mode), which is far cheaper than Pro.

While still in preview, it already shows strong performance on coding tasks due to its massive context. You can try it by picking gemini-2.5-flash-preview on aiToggler.

5. DeepSeek V3 (March 2024)

DeepSeek V3 is an open-source Mixture-of-Experts model (671B total, 37B active). It was trained with efficient techniques to give strong coding ability.

In public benchmarks, DeepSeek V3 outperforms many other open models on coding. For example, on the HumanEval coding test it scored 65.2% pass@1, compared to 53.0% for Qwen2.5-72B and 54.9% for Llama3. It also scored higher on the MBPP coding tasks.

Overall, the developers note DeepSeek-V3 “outperforms other open-source models and achieves performance comparable to leading closed-source models”.

DeepSeek V3 is fully open-source (MIT license), so there is no subscription cost – just the hardware cost to run it. You can test it on aiToggler using the model ID deepseek-v3.

6. Qwen3

Qwen3 is Alibaba’s latest open-source AI model, introduced in April 2025.

It features hybrid reasoning capabilities, allowing it to switch between “thinking” mode for complex tasks like coding and “non-thinking” mode for quicker responses. This flexibility enhances its adaptability for various coding applications.

Trained on a dataset of 36 trillion tokens across 119 languages, Qwen3 demonstrates strong multilingual support, beneficial for global development teams.

In coding benchmarks, the flagship model, Qwen3-235B-A22B, achieved a LiveCodeBench score of 70.7 and a CodeForces Elo rating of 2056, indicating robust performance in code generation and problem-solving tasks.

All Qwen3 models are open-sourced under the Apache 2.0 license, making them freely accessible to developers. You can experiment with Qwen3 on aiToggler by selecting your desired Qwen 3 model (thinking and non-thinking).

Conclusion

These models each bring strong coding skills from different sources. Some focus on raw reasoning power (like OpenAI O3 and Google Gemini Pro), others on efficiency or special features (like Gemini Flash and DeepSeek’s MoE design).

All of them can be toggled on through the aiToggler platform and give developers more options beyond ChatGPT. Try them out with the parallel chat feature to see which best fits your coding projects.