| Signal | GPT-5.4 Nano | Delta | Grok 4.20 Multi-Agent |
|---|---|---|---|
Capabilities | 100 | +17 | |
Benchmarks | 90 | +4 | |
Pricing | 99 | +5 | |
Context window size | 80 | -10 | |
Recency | 100 | -- | |
Output Capacity | 85 | +65 | |
| Overall Result | 4 wins | of 6 | 1 wins |
Score History
78.8
current score
Grok 4.20 Multi-Agent
right now
87.4
current score
OpenAI
xAI
GPT-5.4 Nano saves you $417.50/month
That's $5010.00/year compared to Grok 4.20 Multi-Agent at your current usage level of 100K calls/month.
| Metric | GPT-5.4 Nano | Grok 4.20 Multi-Agent | Winner |
|---|---|---|---|
| Overall Score | 79 | 87 | Grok 4.20 Multi-Agent |
| Rank | #45 | #18 | Grok 4.20 Multi-Agent |
| Quality Rank | #45 | #18 | Grok 4.20 Multi-Agent |
| Adoption Rank | #45 | #18 | Grok 4.20 Multi-Agent |
| Parameters | -- | -- | -- |
| Context Window | 400K | 2000K | Grok 4.20 Multi-Agent |
| Pricing | $0.20/$1.25/M | $2.00/$6.00/M | -- |
| Signal Scores | |||
| Capabilities | 100 | 83 | GPT-5.4 Nano |
| Benchmarks | 90 | 86 | GPT-5.4 Nano |
| Pricing | 99 | 94 | GPT-5.4 Nano |
| Context window size | 80 | 90 | Grok 4.20 Multi-Agent |
| Recency | 100 | 100 | GPT-5.4 Nano |
| Output Capacity | 85 | 20 | GPT-5.4 Nano |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 79/100 (rank #45), placing it in the top 85% of all 290 models tracked.
Scores 87/100 (rank #18), placing it in the top 94% of all 290 models tracked.
Grok 4.20 Multi-Agent has a 9-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.
GPT-5.4 Nano offers 82% better value per quality point. At 1M tokens/day, you'd spend $21.75/month with GPT-5.4 Nano vs $120.00/month with Grok 4.20 Multi-Agent - a $98.25 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. GPT-5.4 Nano also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (2000K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($1.25/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (87/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Grok 4.20 Multi-Agent has a moderate advantage with a 8.600000000000009-point lead in composite score. It wins on more signal dimensions, but GPT-5.4 Nano has specific strengths that could make it the better choice for certain workflows.
Best for Quality
GPT-5.4 Nano
Marginally better benchmark scores; both are excellent
Best for Cost
GPT-5.4 Nano
82% lower pricing; better value at scale
Best for Reliability
GPT-5.4 Nano
Higher uptime and faster response speeds
Best for Prototyping
GPT-5.4 Nano
Stronger community support and better developer experience
Best for Production
GPT-5.4 Nano
Wider enterprise adoption and proven at scale
by OpenAI
| Capability | GPT-5.4 Nano | Grok 4.20 Multi-Agent |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
OpenAI
xAI
GPT-5.4 Nano saves you $8.94/month
That's 83% cheaper than Grok 4.20 Multi-Agent at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | GPT-5.4 Nano | Grok 4.20 Multi-Agent |
|---|---|---|
| Context Window | 400K | 2M |
| Max Output Tokens | 128,000 | -- |
| Open Source | No | No |
| Created | Mar 17, 2026 | Mar 31, 2026 |
The premium reflects Grok's 2M token context window (5x larger than GPT-5.4 Nano's 400K) which enables complex multi-file codebases and architectural analysis that smaller contexts simply cannot handle. At $6/M output tokens, you're paying for the ability to process entire repositories in a single pass, making it cost-effective for large-scale refactoring despite the higher per-token cost.
GPT-5.4 Nano's function calling capability makes it the only viable choice here despite ranking #13 vs Grok's #4. The $0.20/M input pricing means you can afford 10x more user interactions than Grok's $2/M input cost, critical for a consumer-facing code assistant where usage patterns are unpredictable.
GPT-5.4 Nano's explicit 128K output guarantee means you can reliably generate 50,000+ lines of code in a single response at $1.25/M tokens. Grok's null output specification combined with its 68/100 score suggests superior code quality but requires chunking strategies for large generations, potentially negating its context window advantage.
At $0.20/M input tokens, GPT-5.4 Nano costs 90% less than Grok for image processing, making it viable for continuous integration pipelines that analyze hundreds of diagrams daily. However, Grok's 2M context window allows processing entire design documents with embedded images in one pass, potentially saving more through reduced API calls despite the 10x higher base rate.
Grok's architecture appears optimized for autonomous multi-agent workflows (hence the name) that handle orchestration internally rather than through external function calls, evidenced by its 68/100 score without this feature. The 2M context window at $2/M input suggests xAI is targeting enterprise-scale monorepo analysis where agents coordinate through shared context rather than API boundaries.