Model comparison
GPT-5.6 Terra vs Grok 4.20 Multi-Agent
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 45.5 on the Noometry Index. Grok 4.20 Multi-Agent costs 2.9× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. GPT-5.6 Terra scores higher in 8 categories and Grok 4.20 Multi-Agent in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.5 to 36.6.
- The biggest single-benchmark swing is NYT Connections (extended): 78.4% for GPT-5.6 Terra and 89.6% for Grok 4.20 Multi-Agent.
- Grok 4.20 Multi-Agent is cheaper at $1.25 / $2.50 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 1M.
Side by side
| GPT-5.6 Terra | Grok 4.20 Multi-Agent | |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 59.1 | 45.5 |
| Released | 2026-07-09 | 2026-03-09 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1M |
| Max output | 128K | 30K |
| Input $ / M tokens | $2 | $1.25 |
| Output $ / M tokens | $12 | $2.50 |
| Results tracked | 52 | 20 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.1 (#16), Grok 4.20 Multi-Agent: 42.4 (#91)
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Coding | 1484 | 1458 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1520 | — |
| SciCode | 55% | — |
| WeirdML | 78.3% | — |
| ALE-Bench | 1,951 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Terra: 40.2 (#23), Grok 4.20 Multi-Agent: —
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| APEX-Agents | 58.2% | — |
| BALROG | 53.2% | — |
| GDP.pdf | 24.7% | — |
| LMArena Search | — | 1204 |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.6 (#19), Grok 4.20 Multi-Agent: 42.4 (#44)
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| NYT Connections (extended) | 78.4% | 89.6% |
| LMArena Hard Prompts | 1467 | 1448 |
| ARC-AGI-2 | 83.9% | — |
| SimpleBench | 48.9% | — |
| Kagi LLM Benchmark | 51.3% | — |
| ARC-AGI-1 | 96.5% | — |
| CritPt | 30% | — |
| Chess Puzzles | 54% | — |
| Mystery Game Puzzles | 35% | — |
| DTBench | 93.3% | — |
| LMCA | 55% | — |
| Surface Evolver Bench | 83.8% | — |
| Epoch Capabilities Index | 159.31 | — |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.5 (#10), Grok 4.20 Multi-Agent: 36.6 (#106)
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Math | 1463 | 1441 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| OTIS Mock AIME 2024-2025 | 99.7% | — |
| ProofBench | 74% | — |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.3 (#27), Grok 4.20 Multi-Agent: 39.7 (#116)
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Expert | 1492 | 1445 |
| GPQA Diamond | 93.3% | — |
| SimpleQA Verified | 43.2% | — |
Multimodal GPT-5.6 Terra leads
GPT-5.6 Terra: 47.5 (#10), Grok 4.20 Multi-Agent: 39.8 (#44)
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Vision | 1271 | 1259 |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual Too close to call
GPT-5.6 Terra: 54.5 (#42), Grok 4.20 Multi-Agent: 54.6 (#40)
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Non-English | 1439 | 1440 |
| LMArena Chinese | 1514 | 1475 |
| LMArena French | 1476 | 1467 |
| LMArena German | 1462 | 1456 |
| LMArena Japanese | 1446 | 1405 |
| LMArena Korean | 1420 | 1416 |
| LMArena Russian | 1450 | 1457 |
| LMArena Spanish | 1455 | 1447 |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.6 (#38), Grok 4.20 Multi-Agent: 75.0 (#80)
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Instruction Following | 1453 | 1420 |
Long Context Too close to call
GPT-5.6 Terra: 44.4 (#64), Grok 4.20 Multi-Agent: 43.7 (#84)
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Longer Query | 1452 | 1430 |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.4 (#21), Grok 4.20 Multi-Agent: 64.1 (#57)
| Benchmark | GPT-5.6 Terra | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Text | 1446 | 1450 |
| LMArena Creative Writing | 1411 | 1435 |
| LMArena Multi-Turn | 1451 | 1451 |
| EQ-Bench Creative Writing | 1855 | — |
| EQ-Bench 4 | 1234 | — |
Frequently asked questions
Is GPT-5.6 Terra better than Grok 4.20 Multi-Agent?
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 45.5 on the Noometry Index. Grok 4.20 Multi-Agent costs 2.9× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Terra or Grok 4.20 Multi-Agent?
Grok 4.20 Multi-Agent is cheaper. It lists at $1.25 per million input tokens and $2.50 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or Grok 4.20 Multi-Agent better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.1 versus 42.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 1M.
How many benchmarks do GPT-5.6 Terra and Grok 4.20 Multi-Agent share?
19 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and Grok 4.20 Multi-Agent has 20.