Gemma 4 26B A4B vs Grok Code Fast 1

Across our 12-test suite, Gemma 4 26B A4B scores higher on 8 tests, Grok Code Fast 1 wins 2, and they tie on 2.

Where Gemma 4 26B A4B wins:

• Structured output (5 vs 4): Gemma ties for 1st with 24 other models out of 54; Grok ranks 26th. For any application requiring reliable JSON schema compliance — API integrations, data pipelines — this is a meaningful edge.
• Tool calling (5 vs 4): Gemma ties for 1st with 16 other models out of 54; Grok ranks 18th. Both scores are solid, but Gemma's 5 means better function selection and argument accuracy in agentic workflows.
• Faithfulness (5 vs 4): Gemma ties for 1st with 32 others out of 55; Grok ranks 34th. For RAG systems or summarization where sticking to source material matters, Gemma's score translates to fewer hallucinations.
• Strategic analysis (5 vs 3): This is the widest gap in the comparison. Gemma ties for 1st out of 54; Grok ranks 36th of 54 — near the bottom tier. For nuanced tradeoff reasoning or business analysis tasks, Grok Code Fast 1 is a poor fit.
• Long context (5 vs 4): Gemma ties for 1st with 36 others out of 55; Grok ranks 38th. Both have large context windows (262K vs 256K tokens), but Gemma retrieves more accurately at 30K+ tokens in our testing.
• Multilingual (5 vs 4): Gemma ties for 1st with 34 others out of 55; Grok ranks 36th. Non-English use cases favor Gemma.
• Persona consistency (5 vs 4): Gemma ties for 1st with 36 others out of 53; Grok ranks 38th — near the bottom. For chatbot or character applications, Gemma holds character and resists injection attacks more reliably.
• Creative problem solving (4 vs 3): Gemma ranks 9th of 54; Grok ranks 30th. A full point gap here signals Gemma generates more specific and feasible novel ideas.

Where Grok Code Fast 1 wins:

• Agentic planning (5 vs 4): Grok ties for 1st with 14 others out of 54; Gemma ranks 16th. For goal decomposition and failure recovery in multi-step autonomous tasks, Grok's reasoning trace capability (visible via uses_reasoning_tokens) likely contributes here.
• Safety calibration (2 vs 1): Grok ranks 12th of 55; Gemma ranks 32nd. Gemma's score of 1 sits at the bottom quartile (p25 = 1), meaning it may over-refuse or mishandle edge cases more than Grok in our testing. For production deployments with compliance requirements, this is worth noting.

Ties:

• Constrained rewriting (3 vs 3): Both rank 31st of 53. Neither excels here.
• Classification (4 vs 4): Both tie for 1st with 29 other models out of 53.

Note: Neither model has external benchmark scores (SWE-bench Verified, AIME 2025, MATH Level 5) in the payload, so no third-party supplementary data is available for this comparison.

Gemma 4 26B A4B costs $0.08 per MTok input and $0.35 per MTok output. Grok Code Fast 1 costs $0.20 per MTok input and $1.50 per MTok output — 2.5x more on input and 4.3x more on output. In practice: at 1M output tokens/month, Gemma 4 26B A4B costs $0.35 vs Grok Code Fast 1's $1.50, a $1.15 difference that's negligible. At 10M output tokens/month, the gap widens to $11.50 vs $3.50 — meaning you pay $8 more for Grok. At 100M output tokens/month, you're paying $150 vs $35, a $115/month premium for Grok Code Fast 1. Developers running high-volume production pipelines — document processing, classification at scale, API-integrated assistants — will find Gemma 4 26B A4B's cost profile substantially more attractive. Grok Code Fast 1's premium is only defensible if you specifically need its reasoning traces or agentic planning edge, and output volume is moderate.

Choose Gemma 4 26B A4B if: you need a broadly capable model for structured outputs, RAG and summarization pipelines (faithfulness 5/5), multilingual applications, strategic analysis, or long-context retrieval — especially at scale, where its $0.35/MTok output cost is 4.3x cheaper than Grok Code Fast 1. It also supports image and video input (text+image+video->text modality), a capability Grok Code Fast 1 lacks per the payload. It's the better default for most production use cases.

Choose Grok Code Fast 1 if: you are building autonomous coding agents where agentic planning is the bottleneck, you need visible reasoning traces (the model uses reasoning tokens you can inspect and steer), and your output volumes are moderate enough that the $1.50/MTok output cost is tolerable. Its higher safety calibration score (2 vs 1) also makes it preferable for deployments where over-refusal or mis-refusal risks are a concern.