Claude Haiku 4.5 vs Gemini 3.1 Pro Preview

Across our 12-test internal benchmark suite, Claude Haiku 4.5 and Gemini 3.1 Pro Preview tie on 7 tests, Haiku 4.5 wins 2, and Pro Preview wins 3. Neither model dominates — the choice comes down to which specific capabilities matter for your use case.

Where Claude Haiku 4.5 wins:

• Tool calling (5 vs 4): Haiku 4.5 scores 5/5, tied for 1st with 16 other models out of 54 tested. Pro Preview scores 4/5 at rank 18 of 54. In practice, this means more reliable function selection, argument accuracy, and multi-step sequencing — critical for agentic and API-integration workflows.

• Classification (4 vs 2): This is the sharpest gap in the comparison. Haiku 4.5 scores 4/5, tied for 1st with 29 other models out of 53 tested. Pro Preview scores 2/5, ranking 51st of 53 — near the bottom of all models we've tested. For routing, tagging, intent detection, or any categorization task, Haiku 4.5 is the clear choice.

Where Gemini 3.1 Pro Preview wins:

• Creative problem solving (5 vs 4): Pro Preview scores 5/5, tied for 1st with 7 other models out of 54 — a tighter top tier, meaning fewer models reach this ceiling. Haiku 4.5 scores 4/5 at rank 9 of 54 (shared by 21 models). For generating non-obvious, feasible ideas, Pro Preview has a real edge.

• Structured output (5 vs 4): Pro Preview scores 5/5, tied for 1st with 24 other models out of 54. Haiku 4.5 scores 4/5 at rank 26 of 54. For strict JSON schema compliance and format adherence in production pipelines, Pro Preview is more reliable.

• Constrained rewriting (4 vs 3): Pro Preview scores 4/5 at rank 6 of 53. Haiku 4.5 scores 3/5 at rank 31 of 53. When compressing text within hard character limits — headlines, ad copy, UI strings — Pro Preview handles constraints more accurately.

Where they tie (7 of 12 tests):

Both models score identically on strategic analysis (5/5, tied for 1st of 54), faithfulness (5/5, tied for 1st of 55), long context (5/5, tied for 1st of 55), safety calibration (2/5, rank 12 of 55), persona consistency (5/5, tied for 1st of 53), agentic planning (5/5, tied for 1st of 54), and multilingual (5/5, tied for 1st of 55). These shared strengths cover a wide range of core capabilities — neither model has a meaningful advantage in reasoning, reliability, or language coverage.

External benchmark data:

Gemini 3.1 Pro Preview has an AIME 2025 score of 95.6% (Epoch AI), ranking 2nd of 23 models with a sole holder of that score — placing it among the very top math reasoning models by that external measure. Claude Haiku 4.5 does not have an AIME 2025 score in our data. This is a meaningful data point: if math-intensive reasoning is your primary workload, Pro Preview's external benchmark performance is strong evidence in its favor, independent of our internal scores.

Claude Haiku 4.5 costs $1.00/M input and $5.00/M output. Gemini 3.1 Pro Preview costs $2.00/M input and $12.00/M output — 2x more expensive on input and 2.4x more expensive on output.

At 1M output tokens/month: Haiku 4.5 costs $5, Pro Preview costs $12 — a $7 difference that barely registers.

At 10M output tokens/month: Haiku 4.5 costs $50, Pro Preview costs $120 — a $70 gap that starts to matter for small teams.

At 100M output tokens/month: Haiku 4.5 costs $500, Pro Preview costs $1,200 — a $700/month difference that is a real budget line for any production deployment.

Who should care: Any team running classification pipelines, high-volume customer support routing, or agentic loops with many LLM calls will feel this gap acutely. Gemini 3.1 Pro Preview's pricing is justified only if you specifically need its advantages in creative problem solving, structured output, constrained rewriting, or its massive 1,048,576-token context window. Note also that Gemini 3.1 Pro Preview uses reasoning tokens (a documented quirk in the payload), which can increase actual token consumption and effective cost beyond the nominal rates.

Choose Claude Haiku 4.5 if:

• Your workload involves classification, routing, or intent detection (scores 4 vs 2 — Pro Preview ranks near the bottom on this test)
• You're building agentic systems with heavy tool calling (scores 5 vs 4, with Haiku 4.5 in a tighter top tier)
• You're running at high volume (10M+ output tokens/month) and need to keep costs under control — Haiku 4.5 is 2.4x cheaper on output
• You want a model that supports top_k sampling (Haiku 4.5 supports it; Pro Preview does not per the payload)
• Your application doesn't require input modalities beyond text and images

Choose Gemini 3.1 Pro Preview if:

• You need the 1,048,576-token context window — Haiku 4.5's 200K context, while large, is a hard ceiling that Pro Preview blows past
• Your workload is creative problem solving or ideation, where Pro Preview scores 5/5 in a tighter competitive tier
• You need strict structured output / JSON schema compliance (5 vs 4)
• You need constrained rewriting — ad copy, headlines, tight character limits (4 vs 3, rank 6 vs rank 31)
• You're working with audio or video inputs — Pro Preview supports text+image+file+audio+video, while Haiku 4.5 is text+image only
• Math-heavy reasoning is central to your use case — Pro Preview scores 95.6% on AIME 2025 (Epoch AI, rank 2 of 23), and Haiku 4.5 has no comparable score in our data
• You can absorb the $0.70–$700/month cost premium depending on your volume tier