Alternative Finder
Find the best alternative to ChatGPT, Claude, Google Gemini, GLM, Kimi, or the OpenAI API using tracked benchmark performance, token pricing, context window size, and open-weight availability.
Finder inputs
BenchLM uses GPT-5.5 as the tracked OpenAI reference for ChatGPT-like performance.
Benchmarks last updated September 15, 2026. Token pricing and context are used to break ties and surface the strongest real-world replacements, not just the absolute benchmark leader.
Popular starting points
Best ChatGPT Alternatives in 2026
chatgpt alternatives
Best Claude Alternatives in 2026
claude alternative
Best Google Gemini Alternatives in 2026
google gemini alternative
Best OpenAI API Alternatives in 2026
openai api alternative
Best GLM Alternatives in 2026
glm alternative
Best Kimi Alternatives in 2026
kimi alternative
Best Free ChatGPT Alternatives in 2026
free chatgpt alternative
Best Open Source ChatGPT Alternatives in 2026
open source chatgpt alternative
Best Claude Alternatives for Coding in 2026
claude alternative for coding
Best current fit for ChatGPT
Qwen3.8 Max
88.5 BenchLM fitQwen3.8 Max is a strong ChatGPT alternative. It beats GPT-5.5 on BenchLM's general use score. Its blended token price is about 100% lower than GPT-5.5. It is also open-weight, so you can self-host or fine-tune it.
Alibaba · Open Weight · 1M context
Qwen3.8 Max is a strong ChatGPT alternative. It beats GPT-5.5 on BenchLM's general use score. Its blended token price is about 100% lower than GPT-5.5. It is also open-weight, so you can self-host or fine-tune it.
BenchLM fit
88.5
Score vs ref
112%
Token cost
100% cheaper
Google · Proprietary · 1M context
Gemini 3.8 Flash is a strong ChatGPT alternative. It beats GPT-5.5 on BenchLM's general use score. Its blended token price is about 87% lower than GPT-5.5.
BenchLM fit
86.6
Score vs ref
109%
Token cost
87% cheaper
Moonshot AI · Pending · 1.05M context
Kimi K3 is a strong ChatGPT alternative. It beats GPT-5.5 on BenchLM's general use score. Its blended token price is about 49% lower than GPT-5.5. It adds a larger 1.05M context window than the tracked ChatGPT reference.
BenchLM fit
85.3
Score vs ref
116%
Token cost
49% cheaper
Google · Proprietary · 1M context
Gemini 3.7 Flash is a strong ChatGPT alternative. It beats GPT-5.5 on BenchLM's general use score. Its blended token price is about 87% lower than GPT-5.5.
BenchLM fit
85.3
Score vs ref
106%
Token cost
87% cheaper
Tencent · Open Weight · 1M context
Hy4 preview is a strong ChatGPT alternative. It retains about 99% of GPT-5.5's general use benchmark profile. Its blended token price is about 100% lower than GPT-5.5. It is also open-weight, so you can self-host or fine-tune it.
BenchLM fit
84.6
Score vs ref
99%
Token cost
100% cheaper
Google · Proprietary · 1M context
Gemini 3.6 Flash is a strong ChatGPT alternative. It beats GPT-5.5 on BenchLM's general use score. Its blended token price is about 75% lower than GPT-5.5.
BenchLM fit
84.2
Score vs ref
106%
Token cost
75% cheaper
Check the tradeoff before switching
The finder answers a narrower question than “what is the best model?”: which replacement keeps enough quality for your workload without breaking the budget or context limit. Open a direct comparison, pricing row, and model profile before changing providers.
Track model shifts before your stack gets outdated
Benchmarks, pricing, and rankings move quickly. Get notified when a better alternative appears for your workflow.
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FAQ
How does BenchLM rank alternatives?
BenchLM scores alternatives from tracked benchmark performance first, then adjusts for token price, context window, and open-weight preference. The weighting shifts depending on whether you choose balanced fit, lower cost, open-weight, or coding performance.
Why does ChatGPT map to GPT-5.5 in this finder?
BenchLM tracks model families rather than closed chat products directly. For ChatGPT-like comparisons, the finder uses GPT-5.5 as the current OpenAI benchmark reference so the ranking stays grounded in measurable model data.
What does cheaper mean in the ranking?
Cheaper uses a blended token-cost estimate with 35% input price and 65% output price. That gives more weight to the output side because many production workflows spend more on generated tokens than prompt tokens.
Can this finder surface open-source or self-hosted options?
Yes. Set model type to open-weight only or switch the goal to open-weight first. That pushes self-hostable models higher and removes proprietary APIs when you want maximum control.