Flash-Lite is Google's pick for high-volume, latency-sensitive work: classification, translation, and RAG retrieval, not deep reasoning or coding agents. Its 1432 LMArena Elo score reflects solid real-world chat quality despite weaker academic-reasoning benchmarks, so save Gemini 3.1 Pro or Gemini 3.5 Flash for tasks that need more careful reasoning.
Gemini 3.1 Flash-Lite scores 86.9% on GPQA Diamond, the headline benchmark Google published at its March 2026 launch. It is Google DeepMind's most cost-efficient Gemini 3 model, a sparse mixture-of-experts transformer with a 1M-token context window built for high-volume, latency-sensitive workloads rather than frontier reasoning.
Where it sits
- $0.563/M$ per 1M tokensBlended price (3:1)Lower is better#20 / 64peer median $1.70/Mvendor price, checked by HokAI
- 301 tok/stokens/sOutput speedHigher is better#6 / 39peer median 90 tok/scited: Artificial Analysis
- --% solvedSWE-bench VerifiedHigher is better-- / 28peer median 78.3%per source, see benchmark scores
- 86.9%% correctGPQA DiamondHigher is better#24 / 44peer median 88.3%per source, see benchmark scores
Cheaper than 70% of the 64 GA models with a published price, mid-pack on GPQA Diamond (rank 24 of 44), and one of 65 whose vendor states it does not train on customer data.
Ranks are against GA models on HokAI that publish the same figure; ties share a rank.
Provider: Google DeepMind · Family: Gemini 3.1
More about Google DeepMind on HokAI
Context window: 1,048,576 tokens · Max output: 65,536
Input modalities: text, image, audio, video, pdf · Output: text, tool-calls
About Gemini 3.1 Flash-Lite
Gemini 3.1 Flash-Lite is Google DeepMind's most cost-efficient model in the Gemini 3 family, first previewed on March 3, 2026 and promoted to general availability on May 25, 2026, when the preview model ID, gemini-3.1-flash-lite-preview, was retired. It sits below Gemini 3.1 Flash and Gemini 3.1 Pro in Google's lineup, built as a sparse mixture-of-experts transformer optimized for high-volume, latency-sensitive traffic such as classification, translation, retrieval, and lightweight agent steps. It replaces Gemini 2.5 Flash-Lite, which Google is shutting down on July 22, 2026.
On Google's published evaluation suite, Flash-Lite scores 86.9% on GPQA Diamond and 76.8% on MMMU Pro for multimodal understanding, with a Chatbot Arena Elo of 1432 and an Artificial Analysis Intelligence Index of 16. Reasoning-heavy benchmarks lag the rest of the lineup: it manages 16.0% on Humanity's Last Exam, well below its Gemini 3.1 and 3.5 siblings. Google has not published an MMLU-Pro, AIME 2025, or SWE-bench Verified score for Flash-Lite, a signal the company isn't positioning this tier for broad academic reasoning or autonomous coding.
The model accepts up to 1,048,576 tokens of input and can generate up to 65,536 output tokens in a single response, a window 5x larger than Claude Haiku 4.5's 200K limit. Beyond the wider Gemini 3 family's general long-context claims, Google has not broken out a recall benchmark for this smaller variant specifically, so treat performance past roughly 500K tokens as good rather than independently confirmed.
Flash-Lite accepts text, image, audio, video, and PDF input and returns text plus tool-call output. It supports function calling, structured JSON output, and four thinking levels, minimal, low, medium, and high, that let developers trade reasoning depth for latency and spend on a per-request basis. It does not produce native audio or image output; Google ships those separately as Gemini 3.1 Flash Audio and Gemini 3.1 Flash Image.
Pricing is well below Gemini 2.5 Flash's $0.30/$2.50 per-1M-token rate on output, despite Flash-Lite scoring higher across reasoning and multimodal benchmarks, and its blended per-token cost undercuts Claude Haiku 4.5 too. Exact current pricing and a sample workload cost are in the pricing FAQ below.
Flash-Lite is available through the direct Gemini API with API key authentication, through Vertex AI with GCP IAM, and through the Gemini Enterprise Agent Platform. Google has not extended distribution to AWS Bedrock or Azure AI Foundry as of mid-2026. As a closed, proprietary model there are no downloadable weights, quantization options, or self-hosting paths.
The model's training data has a knowledge cutoff of January 2025. Google's model card states that, based on Gemini 3.1 Pro's capability assessments, Flash-Lite is unlikely to reach any Critical Capability Level, and that it improves on Gemini 2.5 Flash-Lite for both safety and tone while keeping unjustified refusals low. Output speed measured by Artificial Analysis is about 301 tokens per second, though time-to-first-token runs around 5.2 seconds, slower than the roughly 2-second median for similarly priced models, likely reflecting the model's default thinking behavior.
On the release timeline, gemini-3.1-flash-lite-preview launched March 3, 2026, was marked deprecated May 11, 2026, and was shut down May 25, 2026 in favor of the GA gemini-3.1-flash-lite model ID that teams should pin to going forward. Data handling follows Google's standard Gemini API terms, with enterprise residency and retention controls available through Vertex AI.
Pricing
$0.25 per 1M input tokens and $1.50 per 1M output tokens via the Gemini API and Vertex AI, with thinking tokens billed at the output rate. Cached input is priced at $0.05 per 1M tokens. Context caching can cut repeated-prompt input costs by up to 90% for workloads that reuse the same system prompt or reference document.
What a real job costs
| Job | Input | Output | Total |
|---|---|---|---|
| Summarise a 20-page PDF | $0.0075 | $0.0015 | $0.0090 |
| Support reply | $0.0005 | $0.0004 | $0.0009 |
| One coding agent run | $0.050 | $0.030 | $0.080 |
Budgets: 20-page PDF = 30k in / 1k out · Support reply = 2k in / 300 out · Coding agent run = 200k in / 20k out. Computed from the vendor's per-token prices at render time; cached-input discounts are not applied.
Key Features
- 1M-Token Context Window: A 1,048,576-token input window paired with a 65,536-token output ceiling, well beyond what most similarly priced models offer.
- Four Thinking Levels: Four selectable reasoning depths, from a fast minimal setting to a deep high setting, put latency and spend under direct developer control per request.
- Multimodal Input: Reads text, images, audio, video, and PDF documents in one call and returns parseable JSON or native tool calls for agent pipelines.
- Context Caching: Caching a reused system prompt or reference document cuts its input-token cost by up to 90% versus resending it on every call.
- Cost Efficiency: Priced below most same-tier rivals on a blended per-token basis, with caching further cutting repeat-workload costs; exact per-token rates are in the pricing FAQ.
Pros
- Blended per-token cost runs about 3.4x below Claude Haiku 4.5's, per this record's price comparison.
- The 1M-token context window comfortably handles full support tickets, transcripts, or medium-length codebases in a single call, more headroom than most models in this price tier offer.
- Beats GPT-5 mini and Claude Haiku 4.5 on 6 of 11 benchmarks Google published at launch.
- Runs at roughly 301 tokens per second per Artificial Analysis, among the faster models in its price tier.
Cons
- Humanity's Last Exam (16.0%) is Flash-Lite's weakest published result, trailing the rest of the Gemini 3 lineup; Google has not published an AIME 2025 or MMLU-Pro score for this model to compare.
- Time-to-first-token runs about 5.2 seconds, noticeably slower to start responding than most same-tier models, likely because thinking is enabled by default.
- No native audio or image output; voice and image generation require separate Gemini 3.1 Flash Audio and Flash Image models.
- Not on AWS Bedrock or Azure AI Foundry, limiting multi-cloud deployment options.
Benchmarks
- MMMU-Pro: 76.8% vendor-reported · 08 Sep 2026 — College-level questions that need reading images and diagrams, % correct.
- LMArena Elo: 1432 vendor-reported · 08 Sep 2026 — Rating from blind human votes on which answer is better.
- GPQA Diamond: 86.9% vendor-reported · 08 Sep 2026 — PhD-level science questions that are hard to search for, % correct.
- Humanity's Last Exam: 16% vendor-reported · 08 Sep 2026 — Expert-written questions across many fields, % correct.
- AA Intelligence Index: 16 cited: Artificial Analysis · 08 Sep 2026 — Composite of 10 evaluations run by Artificial Analysis, 0 to 100.
- Output speed: 301 tok/s cited: Artificial Analysis · 08 Sep 2026 — Median tokens written per second as measured by Artificial Analysis.
A benchmark is an exam, not the job. Scores transfer unevenly between tasks, so weigh the one closest to your workload and read every figure with its source.
Frequently Asked Questions
How much do you pay for Gemini 3.1 Flash-Lite?
It runs $0.25 for every 1M input tokens and $1.50 for every 1M output tokens through the Gemini API or Vertex AI, with thinking tokens counted as output and cached input billed separately at $0.05 per 1M tokens. There is a single Standard pay-as-you-go tier; Google has not published a discounted committed-use rate for this model. A workload classifying leads at roughly 400 tokens per response costs a fraction of a cent per request.
Does Gemini 3.1 Flash-Lite have a free plan?
No, Gemini 3.1 Flash-Lite has no free tier. Every request through the Gemini API or Vertex AI is billed per token from the start, so evaluating it means budgeting a small amount for test calls rather than testing for free.
What should you use instead of Gemini 3.1 Flash-Lite?
GPT-4o mini is worth checking when a 128,000-token context window is enough and a lower per-token price matters more than benchmark score. Ministral 3 8B bills a flat rate for input and output tokens, which suits teams that want predictable pricing over a stronger GPQA Diamond result. Gemini 3.5 Flash-Lite is Google's newer sibling in the same family, worth it if you want the latest generation and can accept a higher per-token rate.
Is Gemini 3.1 Flash-Lite better than GPT-4o mini?
The two publish different benchmark suites, but the gap is clear on context and price: Flash-Lite's context window is far larger than GPT-4o mini's 128,000 tokens, while GPT-4o mini undercuts Flash-Lite on input price. Flash-Lite also publishes a GPQA Diamond score that GPT-4o mini has not reported. Pick Flash-Lite for long-document work or when a documented reasoning benchmark matters; pick GPT-4o mini when requests are short and the lowest per-token price wins.
How do you get started with Gemini 3.1 Flash-Lite?
Get a Gemini API key from Google AI Studio, or enable the model in an existing Google Cloud project through Vertex AI. Send a request specifying gemini-3.1-flash-lite as the model ID, since the older preview ID was retired. Set the thinking level to minimal for simple classification or extraction tasks to keep latency and cost predictable, and raise it only for prompts that need multi-step reasoning.
Top Alternatives
- Gemini 3.1 Pro: Pick Flash-Lite for high-volume, low-cost workloads; pick 3.1 Pro when you need its 94.3% GPQA Diamond score.
- Gemini 3.5 Flash-Lite: Pick 3.1 Flash-Lite when price is the deciding factor; pick 3.5 Flash-Lite for Google's newer generation if the higher per-token rate is acceptable.
- GPT-4o mini: Pick Flash-Lite for its much larger context window; pick GPT-4o mini if you're already standardized on OpenAI's ecosystem and want a lower per-token price.
- Ministral 3 8B: Pick Flash-Lite for stronger benchmark scores and a larger context window; pick Ministral 3 8B for its flat input/output token rate.