Intelligence
39.2
39
General reasoning and benchmark headroom.
LimitedGoogle: Gemma 4 31B is a budget-priced multimodal generalist from Google with heavy runtime profile, large context posture, and the clearest fit around long-context research / multimodal.
Benchmark blend
Dev workflow signal
Large
Budget tier
Google: Gemma 4 31B currently reads as a budget multimodal option with large context and a heavy runtime profile.
Decision Strip
Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.
General reasoning and benchmark headroom.
LimitedTTFT 1.27s
SituationalHow much prompt and task state can stay in view.
Above average$0.00 output / 1M
EfficientEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...
Google multimodal profile
Long-context research / Multimodal with large context and heavy runtime.
Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.
Large context headroom supports repo-wide prompts and long research sessions.
Vision-capable routing opens up multimodal review and extraction workflows.
Budget-friendly input pricing is a strength, but raw capability may vary by workload.
Latency profile is better for deliberate runs than rapid back-and-forth chat.
Image-grounded review, multimodal extraction, and UI audit workflows.
Long-context summarization, repo analysis, and policy or document review.
Benchmarks
Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.
Broad reasoning, knowledge depth, and flagship benchmark posture.
Software implementation, debugging quality, and coding benchmark signal.
Long-horizon execution quality and interactive benchmark evidence.
Specs & Pricing
Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.
This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.