Qwen 3.8 Max: Price, API Access, and Open Weights (2026)
Qwen 3.8 Max shipped Aug 3, 2026: $2/$6 per 1M tokens, 1M context, 131K output, open weights next week. Live on ofox as bailian/qwen3.8-max.
Qwen 3.8 Max is out. Alibaba shipped it on August 3, 2026, two weeks after the preview. This is the release where the numbers stopped being rumours.
- Released: August 3, 2026 (preview announced July 19, 2026)
- Price: $2.00 / 1M input, $6.00 / 1M output on QwenCloud
- Context: 1M window (991K max input, 131K max output)
- Size: 2.4T parameters, 95B active
- Open weights: announced for next week, no license or date published yet
- Access today: QwenCloud API, Qwen Studio, QwenWork
- On ofox: yes,
bailian/qwen3.8-max, OpenAI and Anthropic protocols

TL;DR
- The new flagship is cheaper than the old one: $2/$6 against Qwen 3.7 Max’s $2.50/$7.50.
- Max output doubled to 131K tokens; the 1M context window carried over.
- Alibaba will open-source a Max-class model for the first time, along with Qwen 3.8-27B, “next week” (announced, not dated).
- The official benchmark table puts it ahead of Claude Opus 4.8 on most agentic rows and behind Fable 5 on SWE-bench Pro and HLE. It is a vendor table.
- No Kimi K3 column in that table, which is the comparison most people wanted.
- It is already on ofox as
bailian/qwen3.8-max, over both the OpenAI and Anthropic protocols, so it drops into Claude Code without waiting for Alibaba to open a Claude-shaped endpoint.
What Is Qwen 3.8 Max?
Qwen 3.8 Max is Alibaba’s top-end proprietary model, a 2.4-trillion-parameter multimodal system with 95 billion parameters active per token. It is built on the Qwen 3.5 architecture rather than a new one, and Alibaba positions it around long-horizon autonomous work rather than single-turn chat.
Inputs and outputs:
| Supported | |
|---|---|
| Input modalities | Text, image, video (QwenCloud); text and image on ofox, video not listed |
| Output modalities | Text |
| API features | Function calling, structured outputs, context cache, prefix completion |
| Rate limits (QwenCloud) | 2M tokens/min, 15K requests/min |
The launch post leans hard on multi-day autonomy. Its headline demonstration is an unattended coding run that produced 265 commits, 127 PRs and 151 issues over roughly 16 days in a public repository. Whether that generalises to your codebase is the one thing a launch post cannot tell you.
When Was Qwen 3.8 Max Released?
August 3, 2026. The preview came first, and the two dates get conflated in coverage:
- July 19, 2026. Qwen3.8-Max-Preview announced at WAIC in Shanghai. Reachable only via Alibaba’s Token Plan subscription, Qoder, and QoderWork. No pay-as-you-go API, no model card, no benchmark table.
- August 3, 2026. General availability. QwenCloud API opens, prices published, full benchmark table published, open-weights timing announced.
Any piece dated between those two has stale “no public pricing” and “no benchmarks” claims, including anything citing the 10%-of-standard preview discount, which applied only to the Token Plan window.
Is Qwen 3.8 Max Open Source?
Not today. Alibaba says the weights ship next week, the week of August 10, 2026.
What was actually announced:
- Qwen 3.8 Max weights will be released, described as the first time a Qwen-Max-class model gets open weights.
- Qwen 3.8-27B goes open-weights in the same drop, which is the one most people can actually run.
- No license named. No exact date. No model card or activated-parameter breakdown for the open release.
Three things to hold loosely until the drop lands. A vendor’s “next week” is a plan, not a shipping date. The license matters more than the timing for commercial use. Qwen’s open releases have historically shipped under Apache 2.0 (Qwen3-235B-A22B still carries apache-2.0 on Hugging Face), but that is precedent, not a commitment for a Max-class release. And a 2.4T model with 95B active is not a workstation self-host; the 27B is the one with a realistic hardware story.
How Much Does Qwen 3.8 Max Cost?
$2.00 per 1M input tokens and $6.00 per 1M output tokens on QwenCloud, as of the August 3, 2026 snapshot. The new flagship is 20% cheaper than the model it replaces.
| Qwen 3.8 Max (QwenCloud) | Qwen 3.7 Max (Model Studio, Singapore) | |
|---|---|---|
| Input / 1M | $2.00 | $2.50 |
| Output / 1M | $6.00 | $7.50 |
| Implicit cache read / 1M | $0.25 | n/a |
| Explicit cache write / 1M | $2.50 | n/a |
| Explicit cache read / 1M | $0.17 | n/a |
Two notes on that table. The cache rows come from different price sheets and are not directly comparable. ofox’s Qwen 3.7 Max page lists cache read at $0.50/M and cache write at $3.125/M, which is a gateway’s listing rather than Alibaba’s. And Model Studio’s Singapore sheet was showing a limited-time 50% discount on 3.7 Max at the time of the check, so the effective gap can be smaller than list-to-list suggests.
Prices on both sides of this table move. Pull the current number before you build a cost model on it.
What’s the Context Window and Max Output?
1M tokens total, split as 991K max input and 131K max output, with a separate 262K reasoning ceiling.
- Context window: 1M
- Max input: 991K
- Max output: 131K
- Max reasoning tokens: 262K
The output ceiling is the real change. ofox lists Qwen 3.7 Max at 64K max output on the same 1M window, so 3.8 Max doubles what it can emit per response. That matters for the workloads Alibaba targets: a model that plans across 262K reasoning tokens and then has 64K to write the artefact is a model that truncates.
What Do the Official Benchmarks Say?
Alibaba published a full comparison table at launch, and it is a mixed picture rather than a sweep. Every number below is vendor-reported.
Selected rows, Qwen 3.8 Max against the frontier set:
| Benchmark | Qwen 3.8 Max | Opus 4.8 | Fable 5 | GPT-5.6 Sol (max) | Qwen 3.7 Max |
|---|---|---|---|---|---|
| Terminal Bench 2.1 | 86.6 | 84.6 | 84.6 | 88.8 | 74.5 |
| SWE-bench Pro | 67.7 | 69.2 | 80.0 | 64.6 | 60.6 |
| PaperBench | 93.0 | 80.3 | 88.8 | 90.5 | 64.8 |
| GPQA Diamond | 92.6 | 92.0 | 92.6 | 94.1 | 92.4 |
| HLE | 43.6 | 45.7 | 53.3 | 47.2 | 41.4 |
| IFBench | 82.8 | 62.2 | 63.5 | 72.7 | 79.1 |
| OSWorld-Verified | 86.1 | 83.4 | 85.0 | 83.2 | 73.3 |
| MMMU-Pro | 82.3 | 75.6 | 81.2 | 83.0 | 79.0 |
Read it with the usual discount. The competitor Terminal Bench figures are lifted from Artificial Analysis and OpenAI’s own posts rather than re-run by Qwen, and the SWE-bench Pro row used the Claude Code harness at temp 1.0, top_p 0.95, 256K context, a configuration Qwen chose. Several rows are Qwen’s own in-house benchmarks (QwenSWEBench, QwenQoderBench, QwenReactBench), where the model that shares the name is the one being measured.
The gap in the table is louder than anything in it: there is no Kimi K3 column. Moonshot’s open-weight flagship shipped days before the Qwen 3.8 preview and is the model Qwen 3.8 Max is most often asked about. Alibaba benchmarked against three closed Western models and its own predecessor instead.
How Do I Access Qwen 3.8 Max?
Through QwenCloud today. Three first-party routes exist, and one widely-reported fourth needs checking:
- QwenCloud API. Model ID
qwen3.8-max, the developer path. Keys fromhome.qwencloud.com/api-keys. - Qwen Studio.
chat.qwen.ai, the chat interface. - QwenWork. The workplace agent platform, for non-developers.
- Alibaba Cloud Model Studio. SCMP reported global API access on August 3. Model Studio’s English model list and price sheet still topped out at
qwen3.7-maxon our check the same day, so the catalog pages lag the announcement even though the endpoint is shared.
That last point is worth unpacking, because the branding hides it. QwenCloud’s own developer guides call the OpenAI-compatible endpoint at https://dashscope-intl.aliyuncs.com/compatible-mode/v1 and read the key from DASHSCOPE_API_KEY, the same DashScope international host Model Studio uses. QwenCloud is a front end on that infrastructure, not a separate API.
The call is OpenAI-shaped:
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"),
base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
resp = client.chat.completions.create(
model="qwen3.8-max",
messages=[{"role": "user", "content": "Summarize this repo's test strategy."}],
max_tokens=4096,
)
print(resp.choices[0].message.content)
The Route Alibaba Does Not Offer: Anthropic Protocol
Qwen ships an OpenAI-shaped API only, so Claude Code cannot call qwen3.8-max directly. Claude Code speaks the Anthropic Messages protocol and will not negotiate. A gateway closes that gap on day one.
bailian/qwen3.8-max went live on ofox the same day as the launch. A catalog call on August 3, 2026 returned 123 models with the new ID present, and the model page lists it at $2/M input, $6/M output, cache read $0.25/M, cache write $2.50/M, with a 1,131,072-token context length and a 131,072-token completion ceiling. Prices match QwenCloud list on all four rows.
Worth flagging, because it nearly caught us out. The catalog’s supported_endpoints field lists only /v1/chat/completions and /v1/responses, which reads like the Anthropic route is closed. It is not: a live Messages call on August 3, 2026 came back well-formed, stop_reason: end_turn. Send one real request before you plan around that field.
Two lines of shell put it inside Claude Code:
export ANTHROPIC_BASE_URL=https://api.ofox.io/anthropic
export ANTHROPIC_MODEL=bailian/qwen3.8-max
Or keep the Python above and change two arguments:
client = OpenAI(
api_key=os.getenv("OFOX_API_KEY"),
base_url="https://api.ofox.io/v1",
)
resp = client.chat.completions.create(model="bailian/qwen3.8-max", ...)
Which route to take:
| You want | Go direct to QwenCloud | Go through a gateway |
|---|---|---|
| Lowest possible per-token price | Yes, this is list | Matches list here, verify per model |
| Day-zero access to Qwen-only features | Yes | Lags by however long the gateway takes |
| Claude Code / Anthropic-protocol tools | Not possible today | Yes |
| One key across Qwen, Claude, GPT, Gemini | No | Yes |
| Failover when one vendor rate-limits you | You build it | Built in |
The honest caveat: a gateway adds a hop you do not control, and its price sheet can lag a mid-cycle vendor change. Read the model page, not the marketing page, on ours as much as anyone else’s.
Qwen 3.8 Max vs Qwen 3.7 Max
Cheaper, twice the output ceiling, and a large jump on agentic benchmarks.
| Qwen 3.8 Max | Qwen 3.7 Max | |
|---|---|---|
| Released | 2026-08-03 | 2026-05-19 |
| Parameters | 2.4T (95B active) | Not disclosed |
| Context / max output | 1M / 131K | 1M / 64K (ofox listing) |
| Input / output per 1M | $2.00 / $6.00 | $2.50 / $7.50 |
| Terminal Bench 2.1 | 86.6 | 74.5 |
| SWE-bench Pro | 67.7 | 60.6 |
| On ofox | Yes, bailian/qwen3.8-max | Yes, bailian/qwen3.7-max |
Pick 3.8 Max unless you have a reason not to. It is cheaper, writes twice as much per response, and both are reachable with the same key, so the migration is a model-ID swap rather than an integration. Stay on 3.7 Max only if you have pinned a version for reproducibility or your evals are calibrated against it. The price gap is 20%, not an order of magnitude, so cost alone will not force the move. The 3.7 Plus vs 3.7 Max benchmark comparison covers the cheaper sibling if cost is the deciding factor.
For the cross-vendor question this launch reopens, the Kimi K3 vs GPT-5.5 and Opus 4.8 comparison has the open-weight side of it, and how to use Kimi K3 covers access. Both are on ofox today, K3 as moonshotai/kimi-k3 and this one as bailian/qwen3.8-max, so comparing them is a model-ID swap rather than a procurement decision.
FAQ
When did Qwen 3.8 Max come out? August 3, 2026, as a general-availability release with published pricing and benchmarks. A preview build was announced July 19, 2026, limited to Alibaba’s Token Plan, Qoder and QoderWork.
Is Qwen 3.8 Max free? No. It is priced per token on QwenCloud at $2/$6 per 1M. The July preview ran at a reported 10% of standard pricing for Token Plan subscribers; that window is over.
Can I download Qwen 3.8 Max weights? Not yet. Alibaba announced an open-weights release for the week of August 10, 2026, without a license or exact date. Qwen 3.8-27B is in the same announced drop and is the realistic self-host target.
Does Qwen 3.8 Max support vision? Yes for images, with a caveat on video. The QwenCloud model page lists text, image and video as input modalities, and Alibaba’s multimodal table reports 82.3 on MMMU-Pro and 86.1 on OSWorld-Verified. Through ofox, an image sent on August 3, 2026 was described correctly and raised prompt tokens from 60 to 90, so image input is live there. Video is not in the ofox catalog entry, so do not assume it.
Can I use Qwen 3.8 Max with Claude Code?
Not against QwenCloud directly, because Claude Code speaks the Anthropic Messages protocol and QwenCloud is OpenAI-shaped. Through a gateway that speaks both it works on day one, tested on August 3, 2026: point ANTHROPIC_BASE_URL at https://api.ofox.io/anthropic and set ANTHROPIC_MODEL=bailian/qwen3.8-max.
What is the difference between Qwen 3.8 Max and Qwen 3.8-27B? Max is the 2.4T proprietary flagship available now via API. The 27B is a much smaller model announced for the open-weights drop next week. They share a generation, not a size class.
Is Qwen 3.8 Max better than Kimi K3? Alibaba did not publish that comparison. There is no K3 column in its benchmark table. The two are also not comparable on availability: K3 has open weights and broad gateway support today, 3.8 Max does not.
Sources Checked for This Refresh
- https://qwen.ai/blog?id=qwen3.8
- https://x.com/Alibaba_Qwen/status/2084100707423289643
- https://www.qwencloud.com/models/qwen3.8-max
- https://docs.qwencloud.com/developer-guides/text-generation/function-calling
- https://www.alibabacloud.com/help/en/model-studio/model-pricing
- https://www.alibabacloud.com/help/en/model-studio/models
- https://www.scmp.com/tech/article/3362738/alibabas-ai-model-qwen38-max-made-widely-accessible-ahead-open-weights-release
- https://huggingface.co/Qwen/Qwen3-235B-A22B
- https://ofox.io/en/models/bailian/qwen3.8-max
- https://ofox.io/en/models/bailian/qwen3.7-max
- https://ofox.io/llms-full.txt
GET https://api.ofox.io/v1/models(live calls, 2026-08-03: 122 models at 07:30, 123 withbailian/qwen3.8-maxpresent at 08:20)- Live request tests against
bailian/qwen3.8-max, 2026-08-03:POST /anthropic/v1/messages(200, Anthropic-shaped body),POST /v1/chat/completions(200), and one image-input call (200, correct description, prompt tokens 90 against 60 for the same prompt without the image)
Frequently Asked Questions
- When was Qwen 3.8 Max released?
- August 3, 2026. Alibaba's Qwen team published the launch post 'Qwen3.8-Max: A New Bar for Coding and Cowork' that day and opened API access on QwenCloud at the same time. A preview build came earlier: Qwen3.8-Max-Preview was announced on July 19, 2026 and was reachable only through Alibaba's Token Plan subscription, Qoder, and QoderWork. The August 3 release is the general availability step, not the first public sighting.
- Is Qwen 3.8 Max open source?
- Not yet, but Alibaba says it will be. The launch post states this is the first time it will open-source the weights of a Qwen-Max-class model, and that the open weights will be released next week, meaning the week of August 10, 2026. Qwen3.8-27B is going open-weights in the same drop. As of the August 3 check, Alibaba has not published the license, the exact date, or a model card for either release, so treat the timing as announced rather than confirmed.
- How much does Qwen 3.8 Max cost?
- On QwenCloud, $2.00 per 1M input tokens and $6.00 per 1M output tokens as of the August 3, 2026 snapshot. Caching is priced separately: implicit cache reads at $0.25/M, explicit cache writes at $2.50/M, and explicit cache reads at $0.17/M. That undercuts Qwen 3.7 Max, which lists at $2.50 input and $7.50 output on Alibaba Cloud Model Studio's Singapore price sheet, a 20% cut on both sides for the newer flagship.
- What is the context window of Qwen 3.8 Max?
- 1M tokens total, split on the QwenCloud model page as 991K max input and 131K max output, with a separate 262K ceiling on reasoning tokens. The 131K output limit is the notable number: ofox lists Qwen 3.7 Max at 64K max output, so the new model roughly doubles how much it can write in a single response while keeping the same 1M window.
- How do I access Qwen 3.8 Max?
- Three first-party routes as of August 3, 2026: the QwenCloud API, the Qwen Studio chat interface at chat.qwen.ai, and QwenWork for non-developer workflows. The API is OpenAI-compatible. QwenCloud's developer guides point at https://dashscope-intl.aliyuncs.com/compatible-mode/v1 with a DASHSCOPE_API_KEY, the same DashScope international host Alibaba Cloud Model Studio uses. Model Studio's English catalog and price sheet still listed qwen3.7-max as the newest Max on our check, so the catalog pages lag the launch.
- Is Qwen 3.8 Max available on ofox?
- Yes, as bailian/qwen3.8-max. It landed the same day as the launch: a live GET /v1/models call on August 3, 2026 returned 123 models with the new entry present. The ofox model page lists $2/M input, $6/M output, cache read $0.25/M, cache write $2.50/M, a 1,131,072-token context length and a 131,072-token completion ceiling, matching QwenCloud list on every pricing row. Both the OpenAI and the Anthropic protocol answered on a live test the same day. Same key as Claude, GPT and Gemini.
- How many parameters does Qwen 3.8 Max have?
- 2.4 trillion total parameters with 95 billion active per token, built on the Qwen 3.5 architecture. The active count is the one that matters for inference cost and speed, because it means the model routes each token through roughly 4% of its total weights. Alibaba disclosed both numbers in the August 3 launch post, which is a change from the July preview, when only the 2.4T headline figure was public.
- Is Qwen 3.8 Max better than Claude Opus 4.8 or GPT-5.6?
- It depends on the task, and every number below comes from Alibaba's own table. Qwen 3.8 Max leads that table on PaperBench (93.0), IFBench (82.8), and OSWorld-Verified (86.1). It trails on Terminal Bench 2.1 (86.6 against GPT-5.6 Sol max at 88.8), on SWE-bench Pro (67.7 against Fable 5 at 80.0), and on Humanity's Last Exam (43.6 against Fable 5 at 53.3). Vendor-run tables pick their own harnesses, so treat these as a launch claim to verify, not a settled ranking.


