Gemma 4 31B vs Grok 4.20
Gemma 4 31B is the clear choice for most workloads: it matches Grok 4.20 on 9 of 12 benchmarks in our testing, outscores it on agentic planning (5 vs 4) and safety calibration (2 vs 1), and costs roughly 16x less per output token. Grok 4.20's one meaningful advantage is long context performance — it scores 5 vs Gemma 4 31B's 4 and carries a 2M token context window against Gemma 4 31B's 256K — which matters if you're routinely processing multi-million-token inputs. For everything else, paying Grok 4.20's premium is difficult to justify on benchmark evidence alone.
Gemma 4 31B
Benchmark Scores
External Benchmarks
Pricing
Input
$0.130/MTok
Output
$0.380/MTok
modelpicker.net
xai
Grok 4.20
Benchmark Scores
External Benchmarks
Pricing
Input
$2.00/MTok
Output
$6.00/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite, Gemma 4 31B wins 2 categories, Grok 4.20 wins 1, and they tie on 9. The headline is how closely matched these models are despite the dramatic price gap.
Where Gemma 4 31B wins:
- Agentic planning (5 vs 4): Gemma 4 31B scores 5/5, tied for 1st with 14 other models out of 54 tested. Grok 4.20 scores 4/5, ranking 16th of 54. In practice, agentic planning covers goal decomposition and failure recovery — the backbone of multi-step AI agents. This is a meaningful advantage for anyone building autonomous pipelines.
- Safety calibration (2 vs 1): Neither model excels here — both score below the 75th percentile (p75 = 2 in our distribution). Gemma 4 31B scores 2/5, ranking 12th of 55; Grok 4.20 scores 1/5, ranking 32nd of 55. Gemma 4 31B is the safer choice for applications requiring balanced refusal behavior.
Where Grok 4.20 wins:
- Long context (5 vs 4): Grok 4.20 scores 5/5, tied for 1st with 36 other models out of 55 tested. Gemma 4 31B scores 4/5, ranking 38th of 55 — well below average for this test. For retrieval tasks at 30K+ tokens, Grok 4.20 has a real edge in our testing, and its 2M token context window (vs Gemma 4 31B's 256K) amplifies this advantage for truly large-document workloads.
Where they tie (9 tests, all at the same score):
- Tool calling (5/5 each): Both tied for 1st with 16 other models out of 54. Function selection, argument accuracy, and sequencing are equally strong.
- Structured output (5/5 each): Both tied for 1st with 24 other models. JSON schema compliance is a non-differentiator.
- Strategic analysis (5/5 each): Both tied for 1st with 25 other models. Nuanced tradeoff reasoning is equivalent.
- Faithfulness (5/5 each): Both tied for 1st with 32 other models. Neither hallucinates away from source material in our tests.
- Persona consistency (5/5 each): Both tied for 1st with 36 other models.
- Multilingual (5/5 each): Both tied for 1st with 34 other models.
- Classification (4/5 each): Both tied for 1st with 29 other models.
- Constrained rewriting (4/5 each): Both rank 6th of 53, tied with 24 other models.
- Creative problem solving (4/5 each): Both rank 9th of 54, tied with 20 other models.
The data is unambiguous: these two models are nearly identical across our benchmark suite, with Gemma 4 31B holding a narrow edge overall.
Pricing Analysis
Gemma 4 31B costs $0.13/M input and $0.38/M output tokens. Grok 4.20 costs $2.00/M input and $6.00/M output tokens — roughly 15x more on input and 16x more on output.
At 1M output tokens/month: Gemma 4 31B runs $0.38; Grok 4.20 runs $6.00. A $5.62 difference is negligible for individual developers.
At 10M output tokens/month: Gemma 4 31B costs $3.80; Grok 4.20 costs $60.00. That $56 gap starts to matter for small teams.
At 100M output tokens/month: Gemma 4 31B costs $380; Grok 4.20 costs $6,000. A $5,620/month difference is a real line item for any production application.
The pricing gap compounds fast. Given that our benchmarks show near-identical scores across 9 of 12 tests, any cost-conscious team running meaningful token volumes should default to Gemma 4 31B unless they have a specific long-context or context-window-length requirement that only Grok 4.20's 2M token window can satisfy.
Real-World Cost Comparison
Bottom Line
Choose Gemma 4 31B if:
- You're running any production volume above a few million tokens per month — the 16x output cost difference becomes significant fast
- You're building agentic workflows where goal decomposition and failure recovery matter (scores 5 vs 4)
- Your application involves safety-sensitive use cases (scores 2 vs 1 — neither is strong, but Gemma 4 31B is less likely to over-permit)
- Your context needs fit within 256K tokens, which covers the vast majority of real-world tasks
- You want multimodal input including video (Gemma 4 31B supports text+image+video input per the payload)
Choose Grok 4.20 if:
- You're processing documents or conversations that exceed 256K tokens — Grok 4.20's 2M context window is a hard technical requirement in those cases
- Long-context retrieval accuracy at 30K+ tokens is a primary workload (scores 5 vs 4 in our testing)
- Cost is not a constraint and you want a model with xAI's infrastructure or API features (logprobs, top_logprobs) not available in Gemma 4 31B's parameter set
- You're processing file inputs, which Grok 4.20's text+image+file modality supports per the payload
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.