MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
Model Comparison · Head to Head

Qwen3.8-Max vs Claude Opus 4.8

Qwen3.8-Max wins on Overall, Multimodal; Claude Opus 4.8 wins on Coding. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorAlibaba / Anthropic
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose Qwen3.8-Max when its stronger dimensions matter most; choose Claude Opus 4.8 when its stronger dimensions is the priority.

Choose Qwen3.8-Max if you…

  • Top-tier Chinese
  • Balanced multimodal
  • Alibaba Cloud ecosystem
  • Good value
  • Best for: Chinese tasks, Multimodal, Coding

Choose Claude Opus 4.8 if you…

  • Previous flagship still capable
  • Well-proven stability
  • Best for: Deep reasoning, Long documents, Coding
02

Head-to-head aggregate scores

Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.

Qwen3.8-Max Higher overall
Alibaba · #4 overall
Overall82
Coding77
Multimodal92
VS
Claude Opus 4.8
Anthropic · #5 overall
Overall81
Coding87
Multimodal89

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

Specs & pricing — every field side by side

List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.

DimensionQwen3.8-MaxClaude Opus 4.8Verdict
VendorAlibaba (CN)Anthropic (US)Different vendors
Released2026.072026.05Qwen3.8-Max is newer
Overall (rank)82 · #481 · #5Qwen3.8-Max +1
Coding77 · #687 · #4Claude Opus 4.8 +10
Multimodal92 · #289 · #6Qwen3.8-Max +3
Context window1M1MTie
Max output128K128KTie
Effective-context9596Claude Opus 4.8 more reliable
Input $/1M$2.5$5Qwen3.8-Max cheaper
Output $/1M$7.5$25Qwen3.8-Max cheaper
Cache discount80% off90% offClaude Opus 4.8 deeper
Speed~55 tok/s~40 tok/sQwen3.8-Max faster
TTFT0.6s1.0sQwen3.8-Max snappier
Function calling8888Tie
Refusal rate~10%~9%Claude Opus 4.8 less restrictive
English7894Claude Opus 4.8
Chinese9878Qwen3.8-Max
Modalitiestext, imagetext, imageSame
Open weightsNoNoBoth closed
Fine-tuningYesNo
Free tierFree credits for new Alibaba Cloud Bailiang usersNo free tier
SOC2 / no-trainno / yesyes / yes
Private deploymentYesNoQwen3.8-Max

Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

Qwen3.8-Max leads the overall aggregate by 1 points (82 vs 81). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Claude Opus 4.8 remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a clear gap: Claude Opus 4.8 scores 87 against 77. On multi-file edits, SWE-style tickets and long-horizon agent loops Claude Opus 4.8 needs fewer correction turns; Qwen3.8-Max is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Qwen3.8-Max leads multimodal 92 vs 89. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

Qwen3.8-Max is faster in interactive use: ~55 tok/s with 0.6s TTFT versus ~40 tok/s with 1.0s TTFT (about 1.4× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.

Context: window vs usable recall

Nominal windows are 1M for Qwen3.8-Max and 1M for Claude Opus 4.8. Crucially, the larger nominal window does not win on usable recall: Qwen3.8-Max advertises 1M but Claude Opus 4.8 scores higher on effective-context (96 vs 95), i.e. it actually retains more of what it was given.

Price & total cost

Qwen3.8-Max is the cheaper API at $2.5/$7.5 versus Claude Opus 4.8 at $5/$25 per 1M input/output tokens — list input is about 2.0× lower. Cache discounts (Qwen3.8-Max 80% vs Claude Opus 4.8 90%) shift the effective bill, worked out below.

Chinese vs English

English: Qwen3.8-Max 78 vs Claude Opus 4.8 94. Chinese: 98 vs 78. For Chinese-language production, Qwen3.8-Max is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: Qwen3.8-Max 88 vs Claude Opus 4.8 88, so Qwen3.8-Max has the edge on structured tool use. Fine-tuning is available from Qwen3.8-Max. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

Cost worked example — same workload, real token math

Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.

Scenario · per monthQwen3.8-MaxClaude Opus 4.8Gap
List priceno cache applied$475100M in × $2.5  +  30M out × $7.5$1,250100M in × $5  +  30M out × $252.63×gap
With caching90% of inputs cache-hit$29590M cached in × $0.5  +  10M in × $2.5  +  30M out × $7.5$84590M cached in × $0.5  +  10M in × $5  +  30M out × $252.86×gap

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose Claude Opus 4.8 (coding 87 vs 77).
IF you serve real-time users and latency is a product KPI  →  choose Qwen3.8-Max (~55 tok/s, 0.6s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose Qwen3.8-Max ($$2.5/$$7.5 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Claude Opus 4.8 (effective context 96 vs 95).
07

Frequently asked questions

Is Claude Opus 4.8 worth the higher price over Qwen3.8-Max?
At list the input rate is 2.0x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when Claude Opus 4.8's stronger dimensions protect revenue; for routine volume Qwen3.8-Max is the economical pick.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Qwen3.8-Max is about $295/month and Claude Opus 4.8 about $845/month after cache discounts ($475 and $1,250 at list).
Does the bigger context window actually matter?
Nominal windows are Qwen3.8-Max (1M) and Claude Opus 4.8 (1M), but usable recall follows the effective-context score (95 vs 96). Prefer the higher effective-context model for long-document work where nothing can be missed.
How do they differ for Chinese-language and data-residency use?
Qwen3.8-Max is the Chinese model (Chinese score 98, domestic cloud, possible private deployment) while Claude Opus 4.8 is the global model (Chinese 78, overseas API). Pick by language quality, access path and where data must reside.
Which one supports fine-tuning?
Qwen3.8-Max supports fine-tuning; Claude Opus 4.8 does not at this tier. If you plan to adapt the model to a narrow domain, that is a concrete differentiator.
What is the single-line recommendation?
Choose Qwen3.8-Max for Chinese tasks, Multimodal; choose Claude Opus 4.8 for Deep reasoning, Long documents.