Gemma 4 31B vs Grok Code Fast 1

Across our 12-test benchmark suite, Gemma 4 31B wins 8 tests outright and ties the remaining 4. Grok Code Fast 1 wins zero tests.

Where Gemma 4 31B leads:

• Tool calling: 5 vs 4. Gemma 4 31B ties for 1st among 54 models (with 16 others); Grok Code Fast 1 ranks 18th of 54 (tied with 28 others). Tool calling covers function selection, argument accuracy, and sequencing — the core mechanics of any agentic workflow. A one-point gap here is meaningful for developers building multi-step automations.

• Structured output: 5 vs 4. Gemma 4 31B ties for 1st among 54 models; Grok Code Fast 1 ranks 26th. JSON schema compliance and format adherence matter whenever downstream systems consume model output programmatically. This gap suggests Gemma 4 31B is more reliable for data pipelines and API integrations.

• Strategic analysis: 5 vs 3. This is the largest gap in the comparison — two full points. Gemma 4 31B ties for 1st among 54 models (with 25 others); Grok Code Fast 1 ranks 36th of 54 (tied with only 7 others). For nuanced tradeoff reasoning with real numbers — financial analysis, product decisions, technical architecture reviews — Gemma 4 31B is substantially stronger in our testing.

• Faithfulness: 5 vs 4. Gemma 4 31B ties for 1st among 55 models (with 32 others); Grok Code Fast 1 ranks 34th. Faithfulness measures whether a model sticks to source material without hallucinating — critical for summarization, RAG pipelines, and document-grounded tasks.

• Persona consistency: 5 vs 4. Gemma 4 31B ties for 1st among 53 models (with 36 others); Grok Code Fast 1 ranks 38th of 53 — near the bottom. For conversational AI products, customer-facing bots, or any application requiring stable character, this gap is operationally important.

• Multilingual: 5 vs 4. Gemma 4 31B ties for 1st among 55 models (with 34 others); Grok Code Fast 1 ranks 36th of 55. For non-English deployments, Gemma 4 31B is the clear choice.

• Creative problem solving: 4 vs 3. Gemma 4 31B ranks 9th of 54; Grok Code Fast 1 ranks 30th. Gemma 4 31B generates more specific and feasible non-obvious ideas in our testing.

• Constrained rewriting: 4 vs 3. Gemma 4 31B ranks 6th of 53; Grok Code Fast 1 ranks 31st. Compression within hard character limits — important for marketing copy, UI strings, and SEO content — favors Gemma 4 31B.

Where they tie:

• Agentic planning: Both score 5, both tied for 1st among 54 models (with 14 others). Goal decomposition and failure recovery are equal between these two models.

• Classification: Both score 4, both tied for 1st among 53 models (with 29 others). Routing and categorization accuracy is equivalent.

• Long context: Both score 4, both rank 38th of 55. Retrieval accuracy at 30K+ tokens is equal — and notably, neither model distinguishes itself here despite Gemma 4 31B's larger 262K context window versus Grok Code Fast 1's 256K.

• Safety calibration: Both score 2, both rank 12th of 55. Neither model performs well on refusing harmful requests while permitting legitimate ones — a shared weakness relative to top safety-focused models in our pool (the median score is 2, so these are at or near the median, not outliers).

Gemma 4 31B costs $0.13/MTok input and $0.38/MTok output. Grok Code Fast 1 costs $0.20/MTok input and $1.50/MTok output. The output gap is where it matters most, since most applications generate far more output tokens than they consume input tokens.

At 1M output tokens/month: Gemma 4 31B costs $0.38; Grok Code Fast 1 costs $1.50 — a $1.12 difference that's negligible.

At 10M output tokens/month: $3.80 vs $15.00 — Gemma 4 31B saves $11.20/month.

At 100M output tokens/month: $38.00 vs $150.00 — Gemma 4 31B saves $112/month.

At 1B output tokens/month (high-volume production API): $380 vs $1,500 — a $1,120/month savings.

The price ratio is roughly 4:1 on output. For developers building agentic systems — where models generate lengthy reasoning chains, code, and multi-step plans — token volumes compound quickly. Any team operating at 100M+ tokens/month should treat this gap as a significant budget line. The case for Grok Code Fast 1 at this price difference would require a demonstrable quality advantage it does not show in our benchmarks.

Choose Gemma 4 31B if you need a general-purpose AI model for production use. It wins 8 of 12 benchmarks in our testing — including tool calling (5/5), structured output (5/5), strategic analysis (5/5), faithfulness (5/5), and multilingual quality (5/5) — while costing 75% less per output token ($0.38 vs $1.50/MTok). It also accepts image and video input alongside text, giving it a broader modality footprint. At any meaningful token volume, the cost savings are substantial with no quality tradeoff in our testing.

Choose Grok Code Fast 1 if you have a specific need for reasoning token visibility (its uses_reasoning_tokens quirk exposes reasoning traces in the response, which the payload confirms), you are already invested in xAI's infrastructure, or you have a use case that specifically benefits from its agentic coding positioning. Be aware: in our benchmark testing, it ties Gemma 4 31B on agentic planning and scores lower on tool calling and structured output — so the coding-agent claim does not hold up on our metrics. Grok Code Fast 1's 10,000 max output token cap (vs Gemma 4 31B's 131,072) is also a hard constraint for tasks requiring long-form generation.