Qwen3.5 27B (Reasoning)

Qwen3.5 27B (Reasoning) ranks 108 of 578 on the Artificial Analysis Intelligence Index, giving developers a strong quality signal at a comparatively low token price.

The central evaluation question is not whether Qwen3.5 27B is the absolute strongest model available. The supplied data does not support that conclusion. The more useful question is whether its measured intelligence is high enough to justify selecting it over more expensive alternatives. The answer is often yes for applications where inference volume matters.

Qwen3.5 27B has an Intelligence Index score of 33.8. Several adjacent models in the supplied comparison set score 33.7, including Claude 4.1 Opus (Reasoning), GLM-4.7 (Reasoning), GPT-5 (medium), KAT Coder Pro V2, and MiniMax-M2.5. That narrow grouping makes the price difference strategically important.

The model should therefore be treated as a high-value candidate, not as a universally validated default. The research brief contains no verified official positioning, community feedback, or documented failure cases. It also provides no source-backed claims about deployment behavior. Developers should make the final choice through task-specific testing, especially for coding, mathematical reasoning, long-context work, and high-volume production traffic.

Data provided by Artificial Analysis.

Qwen3.5 27B (Reasoning) is the strongest initial shortlist candidate when a developer wants broad measured intelligence without paying frontier-model pricing.

The supplied adjacent models reveal three different selection patterns:

Model	What the comparison suggests	When the alternative may be preferable
Claude 4.1 Opus (Reasoning)	Similar overall Intelligence Index performance with a much higher price point	A workflow has independently verified quality or reliability requirements that justify premium spending
GLM-4.7 (Reasoning)	Similar overall intelligence with visible math and coding evidence in the data brief	Math or coding performance is more important than broad cost efficiency
GPT-5 (medium)	Similar overall intelligence with visible math evidence and a higher price point	Existing platform integration, operational familiarity, or specialized benchmark results outweigh token cost
KAT Coder Pro V2	Similar overall intelligence with visible coding evidence and a lower blended price	The workload is primarily software development and its coding results hold up in internal tests
MiniMax-M2.5	Similar overall intelligence and a lower blended price	A lower-cost general alternative passes the same application evaluation

This comparison does not establish that Qwen3.5 wins every task. It establishes that the model deserves evaluation whenever broad reasoning quality and operating cost are both important. The evidence is especially incomplete for reliability, safety behavior, context handling, and output speed. Those gaps should remain explicit in any production approval.

Qwen3.5 27B (Reasoning) has enough overall benchmark strength to compete for general reasoning workloads, but the available evidence cannot confirm its performance on coding or mathematics.

The model records an Artificial Analysis Intelligence Index score of 33.8 and ranks 108 of 578. That position indicates meaningful breadth across the index, although the supplied brief does not define the index components or explain how the score should map to a particular developer workflow. Developers should read the ranking as a screening signal rather than a guarantee of answer quality.

The closest-model data is more informative about what remains unknown. GLM-4.7 (Reasoning), GPT-5 (medium), and KAT Coder Pro V2 include additional math or coding scores. Qwen3.5 27B does not have those task-specific values in the supplied snapshot. As a result, the evidence supports a broad-intelligence claim, but not a claim that Qwen3.5 is a leading coding model, a leading mathematics model, or a reliable replacement for either specialist.

The measured first-token latency is 0.3 seconds. That is useful for interactive applications, but the snapshot reports no median output-token rate. Developers therefore cannot infer streaming completion time, long-answer responsiveness, or concurrency behavior from the supplied data.

The context window is also listed as unavailable. Long-document retrieval, repository-scale coding, and multi-turn memory should be tested directly. The research brief offers no verified sources for failure modes or community experience, so those areas remain evidence gaps rather than confirmed weaknesses.

Qwen3.5 27B is economically compelling when its measured quality is sufficient and the application processes substantial token volume.

The supplied price is $0.825 per 1M blended tokens, with input tokens at $0.3 per 1M and output tokens at $2.4 per 1M. The blended figure makes the model look inexpensive for balanced workloads. The separate input and output prices show why workload shape matters. Applications that send large prompts and request short answers may benefit more than applications that generate long reasoning traces or extensive documents.

The comparison set creates a clear cost test. Claude 4.1 Opus (Reasoning), GPT-5 (medium), and the other adjacent models provide reference points for deciding whether Qwen3.5’s quality is worth its specific price. KAT Coder Pro V2 and MiniMax-M2.5 have a lower blended price of $0.525 per 1M tokens, so Qwen3.5 is not automatically the cheapest option in the supplied group.

Qwen3.5 becomes less attractive if a cheaper adjacent model delivers equal task success, lower revision rates, or better coding performance. It also becomes less attractive if its reasoning style produces longer outputs than the application needs. The snapshot provides no output-token speed, quota, uptime, rate-limit, or failure-rate evidence. Those missing operational facts prevent a full total-cost assessment.

Developers should compare cost per successful task, not token price alone. That requires measuring answer acceptance, retries, tool calls, and human review in the target workflow.

Qwen3.5 27B (Reasoning) is worth choosing for broad reasoning applications where a strong overall rank matters and the budget cannot support premium model pricing.

A practical evaluation sequence is straightforward. First, test representative prompts against the model’s real input and output patterns. Second, compare its accepted-task rate with KAT Coder Pro V2 and MiniMax-M2.5, which are cheaper in the supplied comparison set. Third, compare it with GLM-4.7 (Reasoning) and GPT-5 (medium) when mathematics or coding is central. Finally, measure latency and streaming behavior under realistic concurrency because the snapshot does not report output throughput.

Qwen3.5 is a good fit for general assistants, structured reasoning, classification with explanation, and other workloads where broad intelligence is more important than a proven specialist score. That recommendation is conditional. The supplied evidence does not confirm the model’s context window, coding index, math index, community reputation, or documented failure scenarios.

Qwen3.5 is a poor choice as an untested universal default. Developers should avoid assuming that a high overall position guarantees repository-scale coding quality, mathematical accuracy, stable tool use, or predictable long-form output. The missing evidence is material, not cosmetic.

The final decision should depend on an internal benchmark built from production-like tasks. If Qwen3.5 matches the required success rate, its price makes it a strong default candidate. If a cheaper adjacent model matches it, the cheaper option deserves priority. If a specialist wins on the core task, broad-index parity should not override that result.

Qwen3.5 27B (Reasoning) has a strong screening profile, but production adoption still depends on unanswered task and operations questions.

The research brief contains no verified official sources, positioning statements, community feedback, or documented failure scenarios. The conclusions above therefore rely on the supplied Artificial Analysis snapshot and clearly separate measured evidence from recommendations. Developers should treat the following questions as release-gate checks.