Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.
Gemini 3.8 Flash is aimed at teams that need a Google model with broad multimodal input, a long context window, a large output limit, and an OpenAI-compatible API. It fits applications such as…
The stated capabilities are broad input support, a 1,048,576-token context, a 65,536-token output limit, and a 54.9 score on HLE-Verified. HLE-Verified is an expert-level benchmark, so the score suggests the model can handle questions that require hard recall, reasoning, and computation. Because it accepts text, image, video, file, and audio, one workflow can pass a document, a recording, and follow-up instructions together. On OrcaRouter, no separate Google SDK is needed: the model is exposed as google/gemini-3.8-flash behind the standard OpenAI-compatible API. That is convenient for pipelines that currently send chat completion requests. A high max output makes the model capable of producing full-length reports, code files, or structured outputs as a single generation. Does this mean the model supports tool calling, code execution, or structured output? The catalog record does not list those features. Any such capability should be tested before you rely on it.
A 1,048,576-token context window allows prompts that contain a large document, a book, an extensive set of files, or a long recorded transcript. This reduces the need for chunking, retrieval, or multi-turn summarization when the source material can fit within the provider's limit. It also lets you include source text, task instructions, and examples in one call. The practical context budget may be smaller if you request a very long completion, because this listing does not state how input and output share the window. For maximum reliability, place the instruction where the model is most likely to respond to it and test with your own prompt layout. If your total request exceeds the provider limit, OrcaRouter will return an upstream error. Long-context calls are billed by token, so wide context is not free; input tokens are charged at $0.75 per 1M. For tasks where most of the window is irrelevant, a shorter prompt may perform just as well at lower cost.
Not every task needs a 1,048,576-token context, multimodal input, or a 54.9 HLE-Verified score. For short text classification, title generation, simple routing, or low-complexity extraction, a cheaper model can often produce acceptable output at lower cost. In OrcaRouter, switching model IDs does not change the overall API pattern, so teams can prototype on a less expensive model and escalate to google/gemini-3.8-flash only when quality or length requires it. If the workload uses only text and small prompts, the large context and media support add no value. If the desired output exceeds 65,536 tokens, this model cannot produce it in one completion. The pricing structure also matters: output tokens cost $3.75 per 1M, five times the input rate. A generation-heavy workload will be dominated by output cost. For such cases, shorter generations or a less expensive output model are often more economical.
HLE-Verified is the verified subset of the Humanity's Last Exam benchmark, which contains hard, expert-level questions that have been checked for correctness. Gemini 3.8 Flash achieves 54.9 on that benchmark according to the OrcaRouter catalog. Higher scores mean the model answers a larger share of these difficult items correctly. A score above 50 indicates that the model handles more than half of the verified HLE items in this evaluation. It is a reference point for difficult knowledge, math, and reasoning tasks, not a full quality profile. This listing gives no other benchmark scores, so MMLU, coding, math, or instruction-following performance is not derived from this number. Production quality varies by task and prompt style, and benchmark numbers can be affected by evaluation methodology. Teams should run private validation examples through OrcaRouter and compare this model with other candidates rather than treating one aggregate score as the only decision criterion.
The maximum output of 65,536 tokens sets an upper bound on the length of one generated response. It matters when the task asks for extended analysis, a long document, or a large structured payload. For short-answer benchmark tasks such as HLE-Verified, the output limit is not usually a bottleneck. For content generation, it removes the need to split output in most common cases. The output limit also interacts with the 1,048,576-token context window, because a prompt that fills the whole context plus a maximum-length output may exceed the provider's total budget unless the provider separates input and output allowances. This catalog record does not specify that accounting rule. In practice, set max_tokens to the largest value you need instead of always using 65,536. Long outputs are billed at $3.75 per 1M tokens, so unnecessary generation raises cost. If the application needs more than 65,536 tokens in one response, this model is not sufficient by itself.
The OrcaRouter catalog does not publish a latency or throughput number for Gemini 3.8 Flash. Response time depends on prompt size, output length, network conditions, concurrency, and provider-side load. The Flash name in Google's model lineup generally signals a more responsive model than larger or deeper product lines, but no concrete speed target appears in this listing. If you are serving user-facing requests, measure end-to-end time through OrcaRouter with realistic payloads. A short answer will appear faster than a long generation because total generation time usually grows with output length. The pricing model does not add a per-request latency fee; you pay by input and output tokens. To reduce wall-clock time, keep unnecessary content out of the prompt, cap max_tokens, and avoid sending large media when it is not required. No speed guarantee is made on this catalog page, so performance-sensitive applications should be load-tested before launch.
This listing gives no separate limitation report. The documented facts are model name, provider, context window, max output, input modalities, price, API access, and one benchmark score. There is no claim of support for tool calling, code execution, image generation, audio output, or structured output in this record. Those features should be verified with a real request before relying on them. A 54.9 HLE-Verified score still means the model misses roughly 45% of the verified expert-level items in that benchmark, so it is not a perfect reasoning engine. The large context window does not guarantee equal attention to all content, and no hallucination, bias, refusal, or domain-accuracy data is listed. Media input is supported, but the catalog does not specify how each media type is tokenized or what limits apply to file size. For safety-critical or regulated output, build your own validation. If a task is outside the supported inputs or outputs, choose a model with a matching catalog entry.
Gemini 3.8 Flash is billed at the provider rate: $0.75 per 1,000,000 input tokens and $3.75 per 1,000,000 output tokens. OrcaRouter adds zero markup on top of that rate. Input tokens cover the text plus the media content submitted to the model, though this catalog does not provide a per-image, per-video, or per-audio token formula. Output tokens cover the text returned by the model. Because the output rate is five times the input rate, long answers can dominate the bill even when the prompt is large. To manage cost, write prompts that provide relevant context and request a concise response when possible. The catalog does not list any session fee, platform fee, or fixed subscription charge. The only specified charges are the per-token amounts. Response usage fields returned by the API should be used to confirm the number of input and output tokens billed for each call.
At $0.75 per million input tokens, a full 1,048,576-token prompt would cost about $0.79 for input before output is generated, based directly on the listed price and context size. A full 65,536-token output would cost about $0.25 at $3.75 per million. A request that uses the full input window and the full output limit would total about $1.04 at provider rates with zero markup. Most real requests use far less, so these values should be treated as upper-bound arithmetic, not a typical bill. Because output tokens are more expensive, the cheapest design is one that sends only the content the model needs and asks for a focused result. Video and audio inputs have no separate itemized price in this record, so the total for media-rich prompts will depend on how the provider tokenizes those inputs. Monitor usage from each response to estimate aggregate spending accurately.
OrcaRouter lists Gemini 3.8 Flash at the provider rate with zero markup, so the per-token prices shown should match the upstream Google provider rate. No extra OrcaRouter charge per token appears in the catalog record. Caching is a separate issue: no prompt-cache hit price, cache discount, or automatic cache setting is included in the provided model facts. You should not assume that repeated identical prefixes will be billed at a lower rate. The absence of a listed cache price means the safest cost model is based on full input token billing. If caching becomes available, it may reduce the effective cost of repeated prompts, but that behavior is not guaranteed in this entry. To control cost without caching, keep shared prompt blocks short, use external storage for large static content where possible, and avoid resending duplicate material unless the task requires it.
When OrcaRouter models are billed at provider rate with zero markup, the price difference between models comes from their upstream rates. Gemini 3.8 Flash costs $0.75 per 1M input tokens and $3.75 per 1M output tokens. Whether another model is cheaper depends on the other model's provider rate, which is not displayed in this entry. Because there is no OrcaRouter per-token fee, comparing prices is direct: you compare the provider rate columns. Price is not the only factor. A cheaper model that produces unusable output may require retries, human review, or additional prompting, eventually making it more expensive than a model with a higher success rate. For short and simple tasks, lower-cost models still have a clear advantage. For hard reasoning or multimodal/long-context work, evaluate total spend per successful response. Measure both quality and cost on your own dataset before selecting a default model ID.
OrcaRouter exposes an OpenAI-compatible API at https://api.orcarouter.ai/v1. Set your base_url to that address and the model field to google/gemini-3.8-flash. An OpenAI SDK or any client that supports OpenAI chat completion requests can then call the model. For example, a Python client would instantiate OpenAI with base_url=https://api.orcarouter.ai/v1 and api_key set to your OrcaRouter key, then call chat.completions.create with model=google/gemini-3.8-flash. The response should include choices and usage fields in the usual style. This means existing code does not need a Google-specific client. For image, video, file, or audio input, the request must use a content format accepted by the model and passed through OrcaRouter; this catalog page does not show the media schema, so test a small request first. Use the model ID exactly as written, including the google prefix.
At minimum, set model to google/gemini-3.8-flash and provide a messages array with the user content. The endpoint is OpenAI-compatible, so standard parameters such as max_tokens, temperature, top_p, stop, and stream may be available, subject to the provider's support. The catalog entry does not list default values or range restrictions for sampling parameters. Because the listed maximum output is 65,536 tokens, requests that set max_tokens higher than that value should be treated with caution; the upstream model may reject or cap them. If your task requires a long response, set max_tokens explicitly to the expected length. If a short response is enough, keep max_tokens low to avoid paying for needless output. The messages array should use the same roles used with other OpenAI-style models. For media input, add the media content in the format used by your API client and verify that OrcaRouter returns a valid completion rather than an input-encoding error.
Yes. Because OrcaRouter uses an OpenAI-compatible API, migration usually involves three changes: set the base URL to https://api.orcarouter.ai/v1, use an OrcaRouter API key, and change the model name to google/gemini-3.8-flash. Code that reads choices[0].message.content and usage should continue to work. Text-only workflows should migrate cleanly. Multimodal workflows are less certain: if your current code sends images with OpenAI-specific content parts, those fields may or may not be accepted exactly as-is by Gemini 3.8 Flash. The catalog record does not include a media conversion specification. Before migrating high-volume or media-heavy traffic, run a small set of identical requests against both endpoints and compare responses and error messages. Keep your existing model ID available as a fallback during migration because the behavior of one model is not guaranteed to be identical to another even through the same API shape.
This catalog page includes only one Google model, so it does not print a side-by-side table against other Gemini variants. Within Google's naming, Flash commonly identifies a model intended for a balance of speed and cost, while other Gemini tiers may target higher capability or different price points. Those claims are not supported by the facts in this entry, so final selection should be based on the per-model catalog fields. Compare context window, max output, input modalities, price, and benchmark score. Gemini 3.8 Flash has a 1,048,576-token context, a 65,536 max output, multimodal input, and a 54.9 HLE-Verified score. A different Gemini model might have a smaller context or a different price, but that information is not provided here. Run the same evaluation prompts through OrcaRouter against each candidate. This is more reliable than assuming that two models with the same provider share the same behavior.
For simple tasks, a cheaper per-token model can beat Gemini 3.8 Flash on cost. Gemini 3.8 Flash charges $0.75 per 1M input and $3.75 per 1M output; another model with a lower rate may be attractive when outputs are short and tasks are straightforward. However, price alone does not determine total cost. If a cheaper model restarts or produces poor answers on complex requests, the extra calls may exceed the savings. Gemini 3.8 Flash's main offsetting strengths are the 54.9 HLE-Verified score, multimodal input, large context, and long output limit. If your workload does not use those strengths, the cheaper model is the rational choice. Use OrcaRouter to run both models over a representative sample and compare accuracy, output length, and total spend per successful result. Context limits also matter because a model with a smaller window may need additional retrieval or splitting, adding integration cost.
Reasoning-specialized models are usually optimized to spend additional computation on hard logic and mathematics. This catalog entry does not include another model for comparison, so the direction below is qualitative. Gemini 3.8 Flash makes sense when your workload needs long context, very long output, or text plus image, video, file, and audio input. Those features can be more important than an additional point on a hard benchmark. The Flash profile also suggests a lower-latency option than a heavier reasoning model, although no speed claim is listed here. If the task is a difficult proof, code analysis, or multi-step planning question, compare google/gemini-3.8-flash against a reasoning model on your own HLE-style prompts. If you do not need deep deliberation, a Flash-class model can avoid higher cost and slower responses. There is no universal winner; the deciding factors are context size, modality support, required latency, output length, and measured task quality.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1https://api.orcarouter.aiimport os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="google/gemini-3.8-flash",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_tokensreasoningreasoning_effortresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p| Input / 1M tokens | $0.750 |
| Output / 1M tokens | $3.75 |
| Cache read / 1M | $0.075 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
What developers are saying this week
GET /api/public/models/google/gemini-3.8-flashOpen @misc{orcarouter_gemini_3_8_flash,
title = {Gemini 3.8 Flash API},
author = {Google},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/google/gemini-3.8-flash}
}Google. (2026). Gemini 3.8 Flash API. OrcaRouter. https://www.orcarouter.ai/models/google/gemini-3.8-flash