Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 1B/4B/12B/27B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.04DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.05Parasail |
| — | $10.50Parasail · bf16 | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 3% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 29.7% |
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | — | 87.8% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 42.4% |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | — | 95.9% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 64.9% |
| Overview | ||
| Company | ||
| Release date | May 14 2024 | Mar 12 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 1.5 FlashvsClaude Haiku 5.5Gemma 3vsClaude Haiku 5.5Gemini 1.5 FlashvsGPT-6.1 SolGemma 3vsGPT-6.1 SolGemini 1.5 FlashvsMuse Spark OpenGemma 3vsMuse Spark OpenGemini 1.5 FlashvsGrok 4.7Gemma 3vsGrok 4.7Gemini 1.5 FlashvsDeepSeek-V4.1-Flash-1017Gemma 3vsDeepSeek-V4.1-Flash-1017Gemini 1.5 FlashvsMistral Large 4Gemma 3vsMistral Large 4Frequently asked questions
Gemini 1.5 Flash and Gemma 3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 1.5 Flash shipped 302 days before Gemma 3, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 1.5 Flash) vs 128k (Gemma 3). Gemini 1.5 Flash is closed, while Gemma 3 is open weight.
Direct benchmark comparisons are unavailable — Gemini 1.5 Flash and Gemma 3 don't publish scores on any of the same benchmarks.