Claude Opus 4.6 vs Gemma 4 31B
For most production use cases that need coding, long-context reasoning, or high safety calibration, choose Claude Opus 4.6 — it wins long-context (5 vs 4) and safety (5 vs 2) in our tests. Gemma 4 31B is the better value if you need strict JSON/schema output, constrained rewriting, or classification (Gemma scores 5 vs Opus 4/3), and it costs dramatically less.
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Claude Opus 4.6
Benchmark Scores
External Benchmarks
Pricing
Input
$5.00/MTok
Output
$25.00/MTok
modelpicker.net
Gemma 4 31B
Benchmark Scores
External Benchmarks
Pricing
Input
$0.130/MTok
Output
$0.380/MTok
modelpicker.net
Benchmark Analysis
Summary of our 12-test suite (scores shown are from our testing):
- Opus wins (3): creative_problem_solving 5 vs 4, long_context 5 vs 4, safety_calibration 5 vs 2. Long-context: Opus’s 5 is tied for 1st ("tied for 1st with 36 other models out of 55 tested") and aligns with its 1,000,000 token context window — this matters when retrieving or reasoning over 30K+ token documents. Safety_calibration: Opus is tied for 1st ("tied for 1st with 4 other models out of 55 tested"), so it refused harmful requests more reliably in our runs.
- Gemma wins (3): structured_output 5 vs 4, constrained_rewriting 4 vs 3, classification 4 vs 3. Structured_output: Gemma’s 5 is tied for 1st ("tied for 1st with 24 other models out of 54 tested"), so it is the better choice when strict JSON/schema adherence matters. Constrained_rewriting and classification wins indicate more accurate compression/labeling behavior in our prompts.
- Ties (6): strategic_analysis (5/5), tool_calling (5/5), faithfulness (5/5), persona_consistency (5/5), agentic_planning (5/5), multilingual (5/5). Both models tie for top ranks in several agentic and cross-lingual tasks (e.g., tool_calling tied for 1st with 16 others), so for planning and tool-selection both perform at the top of our pool. External benchmarks (attribution required): Beyond our internal scores, Claude Opus 4.6 scores 78.7% on SWE-bench Verified (Epoch AI) and 94.4% on AIME 2025 in our data — SWE-bench ranks Opus 4.6 as rank 1 of 12 (sole holder) in that external measure. These external results reinforce Opus’s coding/problem-solving strength but do not change our internal win/tie breakdown.
Pricing Analysis
Claude Opus 4.6: input $5 / mTok, output $25 / mTok. Gemma 4 31B: input $0.13 / mTok, output $0.38 / mTok. Using a 50/50 input/output split as a representative example: 1M tokens = 1,000 mTok → Opus ≈ $15,000 (500*$5 + 500*$25) vs Gemma ≈ $255 (500*$0.13 + 500*$0.38). Scale: 10M tokens → Opus ≈ $150,000 vs Gemma ≈ $2,550; 100M tokens → Opus ≈ $1,500,000 vs Gemma ≈ $25,500. Who should care: high-volume APIs, startups, and cost-sensitive teams should prefer Gemma for throughput/price; teams needing maximal long-context, agentic workflows, or the specific safety profile demonstrated in our testing may justify Opus’s premium.
Real-World Cost Comparison
Bottom Line
Choose Claude Opus 4.6 if you need: high-stakes coding or agentic workflows, massive context (1,000,000 tokens), top safety calibration (score 5 in our tests), or best-in-class long-context reasoning (score 5, tied for 1st). Choose Gemma 4 31B if you need: strict structured output/JSON/schema compliance (5 vs Opus 4), better constrained rewriting and classification, or drastically lower cost (input $0.13 / mTok, output $0.38 / mTok). If budget is a limiting constraint at scale, Gemma is the practical choice; if quality on long-context/agentic tasks is mission-critical, Opus may justify the premium.
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.