Model comparison
gpt-oss-20b vs Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 32.5 on the Noometry Index. gpt-oss-20b costs 43× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
Summary
- They share 16 benchmarks with published results for both. gpt-oss-20b scores higher in 1 category and Grok 4.20 Multi-Agent in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Grok 4.20 Multi-Agent leads 64.0 to 35.5.
- gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $1.25 / $2.50 for Grok 4.20 Multi-Agent.
- Grok 4.20 Multi-Agent accepts more context: 1M tokens versus 131K.
- gpt-oss-20b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-20b | Grok 4.20 Multi-Agent | |
|---|---|---|
| Provider | OpenAI | xAI |
| Noometry Index | 32.5 | 46.2 |
| Released | 2025-08-05 | 2026-03-09 |
| Weights | Open | Proprietary |
| Context window | 131K | 1M |
| Max output | 16K | 30K |
| Input $ / M tokens | $0.018 | $1.25 |
| Output $ / M tokens | $0.09 | $2.50 |
| Results tracked | 34 | 20 |
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Category by category
Coding Grok 4.20 Multi-Agent leads
gpt-oss-20b: 37.6 (#192), Grok 4.20 Multi-Agent: 43.0 (#92)
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Coding | 1306 | 1457 |
| SciCode | 34.4% | — |
| WeirdML | 40.9% | — |
| ALE-Bench | 566.05 | — |
Agentic & Tool Use Not comparable
gpt-oss-20b: 9.3 (#154), Grok 4.20 Multi-Agent: —
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| Terminal-Bench | 3.4% | — |
| LMArena Search | — | 1204 |
Reasoning Grok 4.20 Multi-Agent leads
gpt-oss-20b: 19.3 (#261), Grok 4.20 Multi-Agent: 43.9 (#48)
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Hard Prompts | 1274 | 1448 |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 89.6% |
| CritPt | 1.4% | — |
| Chess Puzzles | 4% | — |
| DTBench | 68% | — |
| LMCA | 14.5% | — |
| Epoch Capabilities Index | 137.82 | — |
Math Too close to call
gpt-oss-20b: 39.4 (#103), Grok 4.20 Multi-Agent: 39.4 (#104)
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Math | 1317 | 1442 |
| OTIS Mock AIME 2024-2025 | 65.3% | — |
| Omni-MATH | 56.5% | — |
Knowledge Grok 4.20 Multi-Agent leads
gpt-oss-20b: 34.6 (#195), Grok 4.20 Multi-Agent: 40.4 (#119)
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Expert | 1258 | 1445 |
| GPQA Diamond | 60.8% | — |
| MMLU-Pro | 74% | — |
| GPQA (HELM) | 59.4% | — |
Multimodal Not comparable
gpt-oss-20b: —, Grok 4.20 Multi-Agent: 40.5 (#48)
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Vision | — | 1259 |
Multilingual Grok 4.20 Multi-Agent leads
gpt-oss-20b: 42.2 (#197), Grok 4.20 Multi-Agent: 54.4 (#43)
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Non-English | 1268 | 1440 |
| LMArena Chinese | 1314 | 1475 |
| LMArena German | 1255 | 1456 |
| LMArena Japanese | 1244 | 1405 |
| LMArena Korean | 1236 | 1416 |
| LMArena Russian | 1278 | 1457 |
| LMArena Spanish | 1267 | 1447 |
| LMArena French | — | 1466 |
Instruction Following Grok 4.20 Multi-Agent leads
gpt-oss-20b: 61.8 (#240), Grok 4.20 Multi-Agent: 74.8 (#84)
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Instruction Following | 1236 | 1420 |
| IFEval | 73.2% | — |
Long Context Grok 4.20 Multi-Agent leads
gpt-oss-20b: 37.9 (#209), Grok 4.20 Multi-Agent: 43.7 (#88)
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Longer Query | 1250 | 1431 |
Writing & Preference Grok 4.20 Multi-Agent leads
gpt-oss-20b: 35.5 (#265), Grok 4.20 Multi-Agent: 64.0 (#59)
| Benchmark | gpt-oss-20b | Grok 4.20 Multi-Agent |
|---|---|---|
| LMArena Text | 1287 | 1450 |
| LMArena Creative Writing | 1201 | 1436 |
| LMArena Multi-Turn | 1268 | 1452 |
| EQ-Bench Creative Writing | 666 | — |
| WildBench | 73.7% | — |
Frequently asked questions
Is gpt-oss-20b better than Grok 4.20 Multi-Agent?
Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 32.5 on the Noometry Index. gpt-oss-20b costs 43× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-20b or Grok 4.20 Multi-Agent?
gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; Grok 4.20 Multi-Agent lists at $1.25 and $2.50.
Is gpt-oss-20b or Grok 4.20 Multi-Agent better for coding?
Grok 4.20 Multi-Agent scores higher on coding benchmarks: 43.0 versus 37.6 in the Noometry coding category.
Which has the bigger context window?
Grok 4.20 Multi-Agent does, with 1M tokens against 131K.
How many benchmarks do gpt-oss-20b and Grok 4.20 Multi-Agent share?
16 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and Grok 4.20 Multi-Agent has 20.