Qwen3.8 Max API
Choose Qwen3.8 Max
A premium reasoning route exposed over three API protocols, for repository-scale coding, long-document analysis, large evidence sets, long-running agents, and complex knowledge work that benefits from a 1,048,576-token working context.
Qwen3.8 Max
Alibaba Tongyi Qwen flagship Max model
qwen3.8-maxRepository-wide engineering, multi-document synthesis, tool-heavy agents over Chat, Responses, or Messages, and difficult tasks where fewer retries or less human correction can justify a premium route.
Qwen3.8 Max pricing
Estimate a request with a full interactive pricing experience. All user groups use the cost-based Qwen3.8 Max rate.
Token calculator
Enter the token mix for one request.One request uses one mode, so pick the one you are pricing. Explicit: send cache_control, pay 125% of the input rate to create the cache, then 10% on every hit. Implicit: nothing to send, no write charge, 20% on every hit.
Estimated request cost
Qwen3.8 MaxMinimum charge: 0.01 credits per request.
Budget guide
Approximate requests using the current token mix.For quick testing
For regular development
For production evaluation
Model pricing
| Model | Context | Input tokens | Output tokens | Cache write tokens | Cache read · implicit | Cache read · explicit |
|---|---|---|---|---|---|---|
Qwen3.8 Maxqwen3.8-max | All context sizes | $1.765 / 1M120 cr / 1M$1.765 official price | $5.295 / 1M360 cr / 1M$5.295 official price | $2.206 / 1M150 cr / 1M$2.206 official price | $0.353 / 1M24 cr / 1M$0.353 official price | $0.177 / 1M12 cr / 1M$0.177 official price |
Qwen3.8 Max
All context sizesUSD and credits are shown per 1M tokens. Cache write only applies to explicit caches. A single request uses either explicit or implicit cache reads, never both. Live backend pricing takes priority over these fallback rates.
Built-in tool pricing
Built-in tools bill on top of tokens and only when the tool actually runs, so a request that never searches never pays these. Anthropic Messages ignores built-in tools upstream, so nothing runs and nothing is charged there.
Qwen3.8 Max API for long-context reasoning and agent workflows
Access Alibaba’s Tongyi Qwen flagship Max model through EvoLink’s unified API. Qwen3.8 Max serves the same model over OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages with full byte passthrough, a 1,048,576-token context window, controllable thinking, and both explicit and implicit prompt caching for repository-scale engineering, long-document analysis, and multi-step tool workflows.
Where Qwen3.8 Max earns a place in a production model stack
A flagship model should not become the default route for every request. Its strongest fit is work where long context, sustained reasoning, or complex tool sequences can reduce retries, handoffs, and human correction.
Repository-scale coding
Use it when an engineering task depends on architecture documents, related services, test history, large diffs, and constraints spread across many files. Measure accepted patches and review time rather than isolated code-generation quality.
Long-document analysis
Keep connected specifications, research papers, contracts, logs, or knowledge-base evidence in one working context. Retrieval and document structure still matter: a 1M-token window does not make irrelevant context useful.
Tool-calling agents across three protocols
Fits multi-step tool selection, structured output, and repeated external actions over Chat, Responses, or Messages. Preserve complete messages, thinking, tool-call IDs, arguments, and tool results across turns.
When a lighter model is the better route
Short chat, classification, rewriting, simple extraction, and latency-sensitive UI actions rarely need maximum reasoning or a 1M context. Keep those on a cheaper route and escalate only when task difficulty justifies the premium.
What sets Qwen3.8 Max apart on EvoLink
These are contract-level capabilities of the EvoLink route, not benchmark claims. Verify latency, quality, and caching behavior on your own tasks before making it a production default.
Three-protocol byte passthrough
OpenAI Chat, OpenAI Responses, and Anthropic Messages all reach the same model with byte passthrough, so thinking, signatures, and cache markers survive the gateway instead of being lossily re-serialized.
Explicit and implicit prompt caching
Explicit cache_control lets you mark reusable prefixes and bills cached reads at 10% of the input rate; implicit caching applies automatically at 20%. The two modes are mutually exclusive per request, and the gateway keeps cache_control markers rather than stripping them.
Reasoning effort you control
Reasoning is on by default at xhigh and can be dialled to medium or low through reasoning_effort. The gateway passes the field through instead of forcing thinking on or off. Do not send reasoning_effort and thinking_budget in the same request.
Thinking must be replayed between turns
This model keeps preserve_thinking enabled by default, so every assistant reasoning_content in the history has to be sent back unchanged and cannot be folded into the content field. Replayed reasoning counts as input tokens and is billed.
Why Qwen3.8 Max can handle these workloads
The model is most useful when long context, sustained reasoning, and reusable prompt prefixes work together. Context capacity alone does not improve an answer; the workload still needs relevant evidence, clear structure, and an output budget.
A 1M-token workspace, not a target to fill
The 1,048,576-token window can keep related code, specifications, and prior tool results available without excessive chunking. Retrieval and context compaction still matter because irrelevant input competes for attention and increases processing cost.
Sustained reasoning needs an output budget
Reasoning and final-answer tokens both contribute to usage. Treat the output limit as capacity rather than a normal completion size, and set a task-appropriate budget with reasoning_effort.
Caching pays off when prefixes stay stable
Repository instructions, system prompts, reference material, and tool schemas create the strongest cache opportunity when their ordering stays consistent. Frequent model or prompt-structure changes can force the long prefix to be processed again.
Function calling, built-in tools, and Structured Output
Qwen lists function calling, built-in tools, and Structured Output for the production model. Treat support as a capability boundary, then validate the exact tool and schema behavior required by your workflow through the EvoLink route before scaling traffic.
What to verify before routing production traffic
A suitable workload can still fail because the integration uses the wrong identifier, the wrong max-token field for an endpoint, or drops agent state between turns. Verify the request surface before evaluating model quality.
Use the qwen3.8-max model ID
Use qwen3.8-max on the EvoLink route for Chat Completions, Responses, and Anthropic Messages.
Use the right max-token field per endpoint
Chat uses max_completion_tokens, Responses uses max_output_tokens, and Anthropic Messages requires max_tokens. Sending the wrong field can be ignored or rejected upstream.
Replay complete thinking and tool state
Multi-turn agents should retain complete messages, thinking and signature blocks, tool-call IDs, arguments, and tool results. Keeping only the final text breaks state continuity even when the context window is large enough.
Send reasoning_content back on every turn
preserve_thinking is enabled by default for this model. Every assistant reasoning_content in the conversation history must be returned unchanged, and it cannot be merged into the content field. Budget for it: replayed reasoning is billed as input tokens.
Create explicit caches on Chat or Messages
Cache reads work on all three protocols, but creating a large explicit cache on the Responses endpoint is the least reliable path upstream. Mark cache_control prefixes on Chat Completions or Anthropic Messages and read them from any protocol.
Compare cost per accepted task, not token price alone
Qwen3.8 Max earns a premium route only when it reduces chunking, retries, failed tool sequences, or human rework on the same production workload. Evaluate identical task sets instead of comparing isolated prompt prices.
If the model produces usable results with fewer retries and less review effort, a cost-based token rate can still lower total task cost. If those gains do not appear, keep the workload on a lighter route.
Compare leading long-context models after workload testing
EvoLinkFirst verify whether Qwen3.8 Max reduces retries and review effort on your tasks. Then compare price, context, caching, and workload fit to choose the production route.
| Model | Qwen3.8 Max | Claude Opus 5 | Kimi K3 |
|---|---|---|---|
| Input / output | $1.765 / $5.295 | $4.75 / $23.75 | $3 / $15 |
| Context | 1M | 1M | 1M |
| Caching | Explicit + implicit | Read + write | Read + write |
| Best for | Repository-wide engineering, multi-document synthesis, tool-heavy agents over Chat, Responses, or Messages, and difficult tasks where fewer retries or less human correction can justify a premium route. | Premium baseline for long-running coding agents, complex review, and judgment-heavy professional workflows. | Moonshot long-context route for repository-scale coding and multi-document reasoning at a mid-tier rate. |
Related models
Claude Opus 5
Premium baseline for long-running coding agents, complex review, and judgment-heavy professional workflows.
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Kimi K3
Moonshot long-context route for repository-scale coding and multi-document reasoning at a mid-tier rate.
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DeepSeek V4
Cost-sensitive baseline for high-volume coding, reasoning, and agent workloads with a 1M context window.
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GPT-5.6
Sol, Terra, and Luna tiers provide a direct comparison for capability, latency, and cost-routing flexibility.
View modelQwen3.8 Max release, evidence, and integration guides
Qwen3.8 Max release and features
Separate the production release, early-channel differences, official capabilities, and open-weight status.
Read guide
Qwen3.8 Max benchmark evidence
Review official results, third-party evidence, and the production test plan EvoLink will run.
Read guide
Qwen3.8 Max vs Qwen3.7 Max
Choose whether to stay, canary, or migrate with a reversible production checklist.
Read guide
Qwen3.8 Max vs Kimi K3
Compare coding, agents, long context, cost, and main/challenger/fallback routing roles.
Read guideQwen3.8 Max API FAQ
What is the Qwen3.8 Max API model ID?
Use qwen3.8-max for Chat Completions, Responses, and Anthropic Messages on EvoLink.
Which API protocols does it support?
Three: OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages. All serve the same model with full byte passthrough.
What max-token field should I use?
Chat uses max_completion_tokens, Responses uses max_output_tokens, and Anthropic Messages requires max_tokens. Sending the endpoint-native field is safest.
Does it support a 1M-token context?
The EvoLink route records a 1,048,576-token context window. Use it to keep genuinely connected files, documents, and agent state together rather than filling it by default.
How does thinking work?
Reasoning is on by default and controllable through reasoning_effort, which accepts xhigh (default), medium, and low. Do not send reasoning_effort and thinking_budget together. The gateway passes these through and preserves thinking and signature blocks on the Messages endpoint.
Do I need to send reasoning_content back in multi-turn chats?
Yes. Qwen3.8 Max enables preserve_thinking by default, so every assistant reasoning_content in the conversation history must be sent back unchanged, and it cannot be merged into the content field. Returned reasoning tokens count as input tokens and are billed.
How does caching work and what does it cost?
Both explicit caching (cache_control) and automatic implicit caching are supported, and a single request uses one of them, never both. An explicit cached read bills at 10% of the input rate, an implicit one at 20%. Writing an explicit cache bills at 125% of input; implicit caching has no separate write charge.
Can I use the OpenAI SDK or Anthropic Messages?
Yes. Keep the same EvoLink API key, select qwen3.8-max, and use compatible Chat Completions, Responses, or Anthropic Messages flows.
Is it a good default for real-time or high-volume requests?
Usually not without testing. Maximum reasoning and long outputs add latency and cost to simple work. Keep short chat, classification, and lightweight extraction on a smaller route.
How is it priced?
Billing uses four token segments—input, output, cache write, and cache read—at cost-based rates, with cache read split into an explicit and an implicit rate. Live backend pricing takes priority over the fallback rates shown here.