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 October 29, 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
What you run today
GPT-5.5
OpenAI · Proprietary- Use-case score
- 74
- Blended price
- $21.25
- Context
- 1M
$5.00 in · $30.00 out. Each alternative below states its quality delta in points and its blended price delta against this model.
Best current fit for ChatGPT
Qwen3.8 Max
89.6 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
89.6
Score vs ref
109%
Token cost
100% cheaper
Trade-off vs GPT-5.5: +7.0 pts on the overall score, as one side has no use-case rows · open weights, so API price depends on the host
StepFun · Open Weight · 1M context
Step 5 Preview is a strong ChatGPT alternative. It beats GPT-5.5 on BenchLM's general use score. Its blended token price is about 90% lower than GPT-5.5. It is also open-weight, so you can self-host or fine-tune it.
BenchLM fit
87.9
Score vs ref
104%
Token cost
90% cheaper
Trade-off vs GPT-5.5: +3.0 pts on the overall score, as one side has no use-case rows · −$19.14 per 1M blended token price
Moonshot AI · Pending · 1.05M context
Kimi K3.1 Pro is a strong ChatGPT alternative. It beats GPT-5.5 on BenchLM's general use score. Its blended token price is about 66% lower than GPT-5.5. It adds a larger 1.05M context window than the tracked ChatGPT reference.
BenchLM fit
87.7
Score vs ref
112%
Token cost
66% cheaper
Trade-off vs GPT-5.5: +9.0 pts on the overall score, as one side has no use-case rows · −$14.05 per 1M blended token price
Anthropic · Proprietary · 1M context
Claude Sonnet 5.5 is a strong ChatGPT alternative. It beats GPT-5.5 on BenchLM's general use score. Its blended token price is about 66% lower than GPT-5.5.
BenchLM fit
87.4
Score vs ref
111%
Token cost
66% cheaper
Trade-off vs GPT-5.5: +8.0 pts on the overall score, as one side has no use-case rows · −$14.05 per 1M blended token price
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
86.2
Score vs ref
104%
Token cost
87% cheaper
Trade-off vs GPT-5.5: +3.0 pts on the overall score, as one side has no use-case rows · −$18.55 per 1M blended token price
Tencent · Open Weight · 1M context
Hy4 preview is a strong ChatGPT alternative. It retains about 97% 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
85.6
Score vs ref
97%
Token cost
100% cheaper
Trade-off vs GPT-5.5: −2.0 pts on the overall score, as one side has no use-case rows · −$21.25 per 1M blended token price
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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Questions
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 I compare against the exact model I run today?
Yes. Choose "Another model I run today" and pick any tracked model. Every alternative then shows its quality delta in points on the selected use-case score and its blended token-price delta against that model, and models from the same creator stay in the list.
What does the deprecation notice above the results mean?
When the model you are replacing has a published deprecation or retirement notice in Radar's public lifecycle feed or BenchLM's first-party lifecycle records, the finder shows the provider's wording, the effective date with its semantics, the provider-named replacement, and the receipt. A named replacement is the provider's designation, not a claim of equivalence. If no notice is shown, none was found in those sources; that is a coverage result, not a promise that the model stays available.
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.