Claude Haiku 4.5 vs Gemma 4 31B

We ran both models across our 12-test suite and compare per-test scores and ranks below (all statements reflect our testing):

• long_context: Claude Haiku 4.5 scores 5 vs Gemma 4. Haiku is tied for 1st of 55 (with 36 others); Gemma ranks 38 of 55 (tied with 16). This means Haiku is measurably better for retrieval/accuracy at 30K+ token scenarios in our tests.
• structured_output: Gemma 4 31B scores 5 vs Haiku 4. Gemma is tied for 1st of 54 (24 others) while Haiku ranks 26 of 54 (27 share this score). In practice, Gemma is more reliable at JSON/schema compliance and strict format adherence.
• constrained_rewriting: Gemma 4 31B scores 4 vs Haiku 3. Gemma ranks 6 of 53 (tied with 24) vs Haiku rank 31 of 53 (22 share this), so Gemma is better for tight-character compression and hard-limited rewriting tasks.
• strategic_analysis: tie (both score 5). Both are tied for 1st of 54 (26 models share that score), so either performs at top-tier for nuanced tradeoff reasoning in our tests.
• creative_problem_solving: tie (both 4). Both rank 9 of 54 (21 models share this), meaning similar quality for non-obvious feasible ideas in our tests.
• tool_calling: tie (both 5), both tied for 1st of 54 (16 share), so function selection/argument accuracy is comparably strong in our testing.
• faithfulness: tie (both 5), both tied for 1st of 55 (32 share), so both stick to source material similarly in our tests.
• classification: tie (both 4), both tied for 1st of 53 (29 share), implying similar routing/categorization accuracy.
• safety_calibration: tie (both 2), both rank 12 of 55 (20 share); neither model stood out on refusal/permit calibration in our testing.
• persona_consistency, agentic_planning, multilingual: all ties (both score 5 and tie for 1st on their respective rankings). Overall win/tie summary in our testing: Gemma wins 2 tests (structured_output, constrained_rewriting), Claude Haiku wins 1 (long_context), and 9 tests tie. That makes Gemma the model with more wins, but most categories are ties at top scores.

Per the payload, Claude Haiku 4.5 charges input $1 and output $5 per mTok; Gemma 4 31B charges input $0.13 and output $0.38 per mTok. The output-cost ratio in the payload is 13.157894736842104 (Haiku output $5 / Gemma output $0.38). Using the per-mTok values as per‑1k‑tokens and assuming a 50/50 input/output token split as an example: Haiku averages $3.00 per 1k tokens -> $3,000 for 1M tokens, $30,000 for 10M, $300,000 for 100M. Gemma averages $0.255 per 1k tokens -> $255 for 1M, $2,550 for 10M, $25,500 for 100M. Who should care: any organization serving high traffic or heavy-generation workloads (10M+ tokens/mo) will see meaningful monthly savings with Gemma; small-scale prototypes or low-volume users will still notice the per-request cost gap but might accept Haiku for specific long-context needs.

Choose Claude Haiku 4.5 if: you need top long-context retrieval fidelity (Haiku scores 5 vs Gemma 4 on long_context and ties for 1st in rank) and you can accept much higher per-mTok cost (output $5 vs $0.38). Choose Gemma 4 31B if: you need reliable structured outputs or constrained rewriting (Gemma scores 5 on structured_output and 4 on constrained_rewriting versus Haiku 4 and 3), want comparable performance on reasoning, tool-calling, and multilingual tasks (many ties), and you need dramatically lower costs (Gemma output $0.38 vs Haiku $5 per mTok).