This review sits beside three others on the site. agnes-free-multimodal-api-review asks whether free multimodal APIs are good enough. kimi-vs-qwen-vs-glm-long-context-review compares the three leading Chinese models on long context. post-aug31-token-cost-comparison-review compares the real token cost of eleven models after price increases. None focuses on the edge this piece covers: handing raw audio and video to a model and asking what it costs. This review only adds up that bill across four columns: audio input price, video input price, output price, and the relief a large context window buys. Capability is sketched, not scored. If you came for "who is smartest", this is the wrong article; it answers "who is cheapest at pushing an hour of meeting audio or a long video into a model".
Scope, Method, and Selection
This review only adds up the bill for feeding audio and video to a model. Four things matter: audio input unit price, video input unit price, output unit price, and how much a large context window spares you from slicing and stitching. We do not rank models on benchmarks.
Every price comes from an official pricing page, dated 2026-09-19: Alibaba Cloud Bailian and DashScope, Google Gemini developer API, Volcengine Ark, and OpenAI. Overseas models are quoted in US dollars per million tokens; domestic in yuan. We do not convert currencies. Where we could not verify a figure, we write "check the official pricing page" rather than invent one.
Three billing traps deserve a flag. First, are modalities priced the same? Some charge one rate across text, image, audio, and video; others list audio at a premium. Second, token conversion: an hour of identical audio yields different token counts under different tokenizers, so confirm the tokens are comparable before comparing per-token prices. Third, does output include thinking? Gemini 3.8 Flash bills thinking tokens as output, as does part of OpenAI, quietly inflating the bill.
We picked four that compete on the audio-video input edge: Qwen3.8-Omni-Flash, the price-cut protagonist with native omnimodality; Gemini 3.8 Flash, native multimodal with a large context; Doubao on Volcengine, domestic video understanding billed by link; and OpenAI GPT-5.x with the realtime family, mature on audio and frame-based on video. Pure-transcription vendors such as iFlytek sit outside the understanding-multimodal table and appear only in the reflection.
Qwen3.8-Omni-Flash: One Price for Every Modality
Alibaba charges one input rate whether tokens came from text, an image, audio, or video. On the Chinese mainland via Bailian, input is 0.8 yuan per million tokens, cache-hit 0.1 yuan, output 2.7 yuan. In international regions, Model Studio lists input 0.15 US dollars per million tokens, cache-hit 0.016, output 0.47. Source: Alibaba Cloud Bailian pricing page and the official launch blog, dated 2026-09-19.
The cut is the thread of this piece. The prior Qwen3.5-Omni-Plus charged 11 US dollars per million tokens for audio input internationally; the new model drops that to 0.15, about 98.6 percent. By official framing, an hour of audio costs more than 98 percent less, and audio plus video more than 93 percent less, pricing a two-minute sample and multiplying by thirty with video at 720p and one frame per second. On the mainland the prior audio rate was about 18 yuan; the new model is 0.8 yuan.
Capability sketch, from the Qwen launch and ai-bot.cn on 2026-09-18. On AliMeeting diarization DER, Qwen scores 3.4 against Gemini 72.6, lower is better, a wide lead. We state honestly the three rows Gemini wins: OmniVideoBench 63.4 against 65.2, LVOmniBench 63.3 against 70.7, and FLEURS WER 9.3 against 7.9. The cut did not collapse capability; Gemini still leads on long-video reasoning and multilingual ASR.
The sweet spot is a 1M-token context that holds about an hour of audio-video, with agentic evidence-gathering cutting token use about 45.7 percent. For an hour of video you skip the slice-extract-stitch pipeline and feed it once. For usage and plugins see the Qwen3.8-Omni-Flash hotspot, the Qwen-MM-Plugins resource, and the API access SOP.
Gemini 3.8 Flash: Pricy on Output and Thinking
Google prices Gemini 3.8 Flash with one rate across all input modalities: text, image, video, audio, and PDF bill at 0.75 US dollars per million input, output 3.75 including thinking, cache-hit 0.075. Source: Google Gemini developer API pricing page, dated 2026-09-19. Two traps: 0.75 and 3.75 are an intro rate through 2026-12-31 and double to 1.50 and 7.50 on 2027-01-01; and output includes thinking tokens, so more thinking means a heavier bill.
Against Qwen, Gemini input at 0.75 is five times Qwen international at 0.15, while output at 3.75 is about eight times Qwen at 0.47. Gemini is more expensive on feed and even more on produce. For a long video, where input is huge and output modest, Gemini's cost sits mainly in input.
Gemini wins the three rows above and offers a 1M context. Its quiet advantage is a mature ecosystem and a separate Live API. The realtime voice line is outside the main table because it runs on per-minute rather than per-token rails.
Doubao and OpenAI: Link Billing and Frame Splitting
Doubao on Volcengine keeps two ledgers. On understanding, Doubao-Seed-2.0-pro accepts a video link; a public link bills about 8.6 yuan per million input and 28.9 yuan output in the 0-32k tier, while a same-region TOS path drops input to about 6.6 yuan. Plain text, image, and video on Seed-2.1-pro cost 6 yuan input, 30 yuan output, 1.2 yuan cache-hit; Seed-2.1-turbo is 3 yuan input, 15 yuan output. Source: Volcengine Ark pricing page and product page, dated 2026-09-19. Doubao's tiered billing by input length is its biggest difference from one-price models; a long video in a higher tier lifts the unit price.
On recognition, Doubao file ASR is 0.8 yuan per hour, streaming 1.0 yuan. That is a "turn into text" price, not an understanding price, so we do not set it beside Qwen and Gemini input rates; we only note that if you only need transcription, hourly ASR is often far cheaper than feeding whole audio as tokens.
OpenAI GPT-5.x supports audio input natively. GPT-5.4 charges 5 US dollars per million for audio input, 20 audio output, 2.5 text input, 15 output; GPT-5.5 charges 10 audio input, 5 text input, 30 output. Source: OpenAI official pricing page cross-checked with trackers, dated 2026-09-19. Video needs a note: GPT-5.x input is mainly text, image, and audio, and native video understanding requires splitting into frames fed as images, with no official "one price per video" basis, so we estimate by image-frame input. The realtime line, gpt-realtime-2, bills audio input about 32 US dollars per million, with a per-minute translation channel near 0.034 US dollars per minute.
OpenAI is the most expensive here: audio input at 5 to 10 US dollars is thirty to sixty-five times Qwen at 0.15. Its strength is reasoning and ecosystem, not cheap audio-video intake. The site's earlier token-cost review also places OpenAI's cost in output and thinking.
The Summary Table: Audio, Video, Output, Context
All prices are 2026-09-19 official snapshots; currencies are not converted. Gemini shows the intro rate, doubling in 2027.
| Model | Audio input | Video input | Output | Cache hit | Context | Basis |
|---|---|---|---|---|---|---|
| Qwen3.8-Omni-Flash | 0.15 USD / M (intl); 0.8 yuan / M (mainland) | same as audio | 0.47 USD / 2.7 yuan | 0.016 / 0.1 | 1M | one price |
| Gemini 3.8 Flash | 0.75 USD / M | same | 3.75 USD (thinking) | 0.075 | 1M | intro, doubles 2027 |
| Doubao Seed-2.1-pro | 6 yuan / M | ~8.6 yuan / M link | 30 yuan | 1.2 | 256k | tiered |
| OpenAI GPT-5.4 | 5 USD / M audio | frame | 15 USD | 0.25 | 400k | audio high |
| OpenAI GPT-5.5 | 10 USD / M audio | frame | 30 USD | 0.5 | 400k | audio high |
The table shows the edge: Qwen pulled feed-side price to text-model levels, Gemini is cheap on intro but expensive on output and will double, Doubao's link and tier billing is unfriendly to long video, and OpenAI audio is priciest. Doubao and OpenAI "video input" is not a native whole-video rate; one bills by link, the other by frame.
A second table adds the capability direction, so cost and fitness sit in one view.
| Benchmark | Qwen3.8-Omni-Flash | Gemini 3.8 Flash | Note |
|---|---|---|---|
| AliMeeting DER (low) | 3.4 | 72.6 | Qwen lead |
| WildClawBench-MM (high) | 71.0 | 58.9 | Qwen |
| OmniVideoBench (high) | 63.4 | 65.2 | Gemini |
| LVOmniBench (high) | 63.3 | 70.7 | Gemini |
| FLEURS WER (low) | 9.3 | 7.9 | Gemini |
Scenario Math: What Three Jobs Cost
Scenarios use transparent assumptions for relative gaps, not precise quotes. Domestic models in yuan, overseas in US dollars, not converted.
Scenario one: an hour of meeting audio to minutes. Assume about 60K input tokens, 2K output. Qwen international: input 60K times 0.15 per million about 0.009 US dollars, output 2K times 0.47 about 0.0009, total about 0.01 US dollars, roughly 0.07 yuan. Gemini: input 0.045, output 0.0075 and higher with thinking, above 0.05. Doubao Seed-2.1-pro audio understanding about 60K times 6 per million about 0.36 yuan; pure ASR at 0.8 yuan per hour avoids token conversion. OpenAI GPT-5.4 audio: 60K times 5 per million equals 0.3 US dollars, about thirty times Qwen. Verdict: meetings are cheapest per token on Qwen and Gemini; pure transcription on Doubao ASR is simple but no understanding.
Scenario two: an hour of video to notes. Assume 720p, one frame per second, plus audio, about 200K input, 3K output. Qwen international: input 200K times 0.15 equals 0.03 US dollars, output 3K times 0.47 about 0.0014, total about 0.03 US dollars, roughly 0.2 yuan. Gemini: input 0.15, output higher with thinking, around 0.2. Doubao Seed-2.0-pro video link public: about 200K times 8.6 about 1.72 yuan input, plus output about 1.9 yuan. OpenAI by frame lands in the Doubao class or higher. Verdict: on long video Qwen's one-price-plus-large-context advantage is sharpest, pressing cost to cents.
Scenario three: realtime voice service. This runs on realtime rails, not Chat Completions. OpenAI gpt-realtime-2 per-minute translation is about 0.034 US dollars per minute, or audio tokens near 32 US dollars per million. Gemini Live API bills separately. Qwen3.8-Omni-Flash-Realtime has shipped but had no standalone pricing row as of 2026-09-19, so check the official page. Realtime compares on latency and concurrency more than unit price.
Reflection: Price War, Token Optimization, Hidden Cost
The price war logic: Qwen cut audio input from 11 to 0.15 US dollars, pulling omnimodality from luxury to commodity. Teams that avoided models over audio cost can now feed long meetings directly. But cuts move; Gemini's intro rate doubles in 2027, so "cheapest today" is not "cheapest forever".
Token optimization matters more than unit price. Qwen says agentic evidence-gathering cuts token use about 45.7 percent, a second discount on top of the unit cut. Coarse-scan-then-evidence versus stuffing the whole clip changes the bill by nearly half. Gemini thinking is the same: dropping the level from high to medium can cut output tokens by a third. Compare whether you can compress tokens before comparing unit price.
Hidden costs outside price. Preprocessing: OpenAI video needs frame splitting, Doubao a link, Gemini sampling control, all engineering that costs staff time. A large video on a TOS path is cheaper than public, but you move it first. A multi-model chain of slice, extract, transcribe, and understand looks cheap per unit but carries high maintenance; a one-feed model like Qwen is low on price and low on engineering. Compliance: domestic workloads on Bailian or Volcengine keep data in-region, a cost absent from any price sheet. Pure-ASR vendors like iFlytek still hold a place in transcription-only scenarios but sit outside the understanding-multimodal table.
Closing: this piece adds up cost, not capability. For real selection, read this edge with the Chinese three-model long-context review and the free multimodal API review.
FAQ
question one: How does this differ from the site's token-cost review?
That piece compares the real token cost of eleven models after price increases, mostly text-centered. This piece focuses only on feeding raw audio and video to a model, counting audio input, video input, output, and context. The two are complementary.
question two: Are Qwen's price-cut numbers reliable?
Audio input fell from 11 US dollars per million tokens, the prior Qwen3.5-Omni-Plus international rate, to 0.15, about 98.6 percent, from the Alibaba pricing page and launch. The more than 98 percent hourly audio and more than 93 percent audio-video cuts are official framing, a two-minute sample times thirty. Figures follow the official pricing page, dated 2026-09-19.
question three: Why does OpenAI have no single video price?
GPT-5.x native input is mainly text, image, and audio; video must be split into frames fed as images, with no official "one price per video" basis, so we estimate by image-frame input. That split is itself OpenAI's hidden engineering cost on this edge.
question four: Doubao hourly ASR or token-based understanding, which to pick?
If you only transcribe, Doubao file ASR at 0.8 yuan per hour is often cheaper than feeding whole audio as tokens; if you need watch-and-reason understanding, use the Seed series rate, about 8.6 yuan per million tokens public for video link. Pick by need.
question five: Gemini intro rate doubles in 2027, can we trust the current price?
0.75 and 3.75 are an intro rate through 2026-12-31, and Google docs state 1.50 and 7.50 from 2027-01-01. This is announced, not a rumor. Long-running workloads must budget the post-doubling run rate, not today's price.