Compare AI models
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.25 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $2.50 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.20 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.2067Relace | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.4176StreamLake | — |
| Benchmarks | ||
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. | 14% | 56% |
CheatBenchCheating rate — Measures how often AI agents try to cheat on difficult assignments, such as reading hidden answers, copying work or manipulating grading. The overall score gives equal weight to ten categories; the sycophancy category measures how far an agent shifts its beliefs toward a user's stated views. Scores describe each model in its tested agent setup, and task success is measured separately. Lower is better. | 75.1% | — |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 76.7% |
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. | 93.5% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 83.4% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | — | 53.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 90.1% | — |
| Overview | ||
| Company | DeepSeek | SpaceXAI |
| Release date | Apr 24 2026 | Feb 17 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4-ProvsClaude Sonnet 5.5Grok 4.20 BetavsClaude Sonnet 5.5DeepSeek-V4-ProvsGPT-6.1 SolGrok 4.20 BetavsGPT-6.1 SolDeepSeek-V4-ProvsGemini 4 ArgonGrok 4.20 BetavsGemini 4 ArgonDeepSeek-V4-ProvsMuse Spark 1.3Grok 4.20 BetavsMuse Spark 1.3DeepSeek-V4-ProvsMistral Large 4Grok 4.20 BetavsMistral Large 4DeepSeek-V4-ProvsKimi K3Grok 4.20 BetavsKimi K3Frequently asked questions
Grok 4.20 Beta leads DeepSeek-V4-Pro on 1 of the 1 benchmark they both report (BullshitBench v2). Only Grok 4.20 Beta has a verified first-party API price: $1.25 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek-V4-Pro. Grok 4.20 Beta shipped 66 days before DeepSeek-V4-Pro, so benchmark comparisons should account for the intervening progress.
DeepSeek-V4-Pro is open weight, while Grok 4.20 Beta is closed.
On BullshitBench v2, Grok 4.20 Beta leads at 56% vs DeepSeek-V4-Pro at 14%.