o3 vs Qwen3.5 122B A10B (Reasoning): The Ultimate Performance & Pricing Comparison

Qwen3.5 122B A10B (Reasoning) is the stronger default for cost-sensitive development workloads, but o3 remains the safer choice when the available math evidence matches the task.

The supplied benchmark snapshot gives Qwen3.5 a 32.3 Artificial Analysis Intelligence Index, compared with 30.4 for o3. Qwen3.5 also posts 138.285 median output tokens per second, while o3 posts 128.056. Both models show 0.3 seconds of latency in the snapshot.

The commercial difference is larger than the measured speed difference. Qwen3.5 is listed at $1.1 per 1M blended tokens, compared with $3.5 for o3. Its input price is $0.4 per 1M tokens, compared with $2 for o3. Its output price is $3.2 per 1M tokens, compared with $8 for o3.

That conclusion has an important boundary. o3 has an 88.3 Artificial Analysis Math Index, while the snapshot provides no comparable Qwen3.5 math value. Qwen3.5 has a 45.7 Coding Index, while no comparable o3 coding value is provided. The evidence supports a practical default, not a universal capability ranking.

Data provided by https://artificialanalysis.ai/

Qwen3.5 122B A10B (Reasoning) leads the measurable general comparison, while o3 has the clearest specialized advantage in the available math data.

The benchmark snapshot is the strongest direct comparison available here: Artificial Analysis data. It does not provide a shared score for every developer concern, so the table should guide routing decisions rather than replace task-specific testing.

The release dates also create a version-status question. The snapshot lists o3 as released on 2025-04-16 and Qwen3.5 as released on 2026-02-24. However, release recency alone does not prove product stability, compatibility, or quality for a particular codebase.

OpenAI's current model directory lists GPT-5.6 Sol, GPT-5.6 Terra, and GPT-5.6 Luna as current frontier models, but does not list o3: OpenAI model directory. That absence makes o3's current API status a deployment risk that the benchmark numbers cannot answer.

Qwen3.5 122B A10B (Reasoning) has the measured speed advantage, but the incomplete benchmark coverage makes workload fit more important than the headline ranking.

The output-speed gap is modest in product terms. Qwen3.5 records 138.285 median output tokens per second, versus 128.056 for o3. Both models record 0.3 seconds of latency. That combination suggests that first-response responsiveness may be similar in the supplied environment, while longer generated answers may finish sooner with Qwen3.5.

The practical impact depends on how much output an application requests. A coding assistant that streams short patches may gain little from the throughput difference. A reasoning workflow that produces long explanations, test plans, or generated files may benefit more from Qwen3.5's higher median output speed. The snapshot does not provide a distribution, tail latency, time-to-first-token detail, or workload-specific measurement, so these implications remain directional.

Capability evidence is asymmetric. Qwen3.5 has a 45.7 Coding Index, but o3 has no supplied Coding Index. o3 has an 88.3 Math Index, but Qwen3.5 has no supplied Math Index. The available 32.3 versus 30.4 Intelligence Index favors Qwen3.5, yet it cannot settle coding or mathematical reliability.

No verified community testing method, coding discussion, or failure-case corpus is included for either model. Developers should therefore treat speed as a useful tie-breaker and validate the dominant task directly before routing all traffic.

Qwen3.5 122B A10B (Reasoning) is the obvious listed-price choice, but o3 can still be economically rational if it prevents expensive retries or review work.

The charted prices show Qwen3.5 at $1.1 per 1M blended tokens and o3 at $3.5. Qwen3.5 is also cheaper on input at $0.4 versus $2, and cheaper on output at $3.2 versus $8. The output difference matters especially for reasoning-heavy applications, where generated explanations, code, and revisions can dominate the bill.

Price alone does not determine total cost. A cheaper model becomes more expensive in practice if it needs repeated calls, produces harder-to-review code, or requires a fallback for mathematical tasks. The supplied data does not report retry rates, acceptance rates, token utilization, tool-call overhead, or human review time. It also does not establish whether both models are available through the same endpoint or under equivalent service conditions.

OpenAI's current pricing page does not list an o3 Standard, Batch, Flex, or Fast mode price: OpenAI API pricing. That creates a more serious commercial issue than a simple price gap. The $2 and $8 values in the benchmark snapshot are useful for comparison, but their current procurement status should be verified before production budgeting.

For a new workload, Qwen3.5 should receive the first cost test. For an existing o3 integration, migration should be justified with measured quality and availability results, not with listed token prices alone.

Data provided by https://artificialanalysis.ai/

Qwen3.5 122B A10B (Reasoning) is the best first candidate for broad developer workloads, while o3 deserves a targeted role for math-sensitive tasks and validated legacy integrations.

Choose Qwen3.5 first for applications where token economics, general intelligence, and output throughput matter together. Examples include code generation, repository exploration, documentation drafting, and agent loops that produce substantial text. The available evidence supports that direction through its 32.3 Intelligence Index, 45.7 Coding Index, 138.285 median output tokens per second, and $1.1 blended price.

Choose o3 when mathematical reasoning is a primary acceptance criterion and the 88.3 Math Index is relevant to your task. This is a conditional recommendation, not proof that o3 is better overall. Qwen3.5 has no supplied Math Index, so the comparison cannot measure whether its reasoning performance is lower, equal, or higher in that area.

Keep o3 in consideration for an existing system only after checking current API availability. The OpenAI model directory does not list o3 among the current models, and the supplied official material does not confirm a stable alias, endpoint, context window, output limit, or successor relationship. Those missing facts can outweigh benchmark quality in a production migration.

A sensible rollout is task-based: test Qwen3.5 on representative coding and agent traces, test o3 on representative math traces, then compare successful outcomes, retries, latency, and review burden. The supplied briefs do not contain those production measurements, so no evidence-based universal routing rule can be stated.

Qwen3.5 122B A10B (Reasoning) should enter validation first, but the missing evidence means production commitment still requires direct testing.

The supplied material does not verify Qwen3.5's official documentation, stable API alias, context window, output limit, or deployment terms. It also does not verify current o3 availability beyond the fact that o3 is absent from the cited current model directory. Developers should confirm these operational details with the intended provider before implementation.