Ling 3.0 Flash VL vs Qwen2.5 Coder 32B InstructWhich Is Better in 2026?
Ling 3.0 Flash VL vs Qwen2.5 Coder 32B Instruct: which should you choose in 2026?
Ling 3.0 Flash VL (by inclusionAI) and Qwen2.5 Coder 32B Instruct (by Qwen) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
Ling 3.0 Flash VL is about 8.3× cheaper on a blended 3:1 input/output basis ($0.09 vs $0.745 per 1M tokens).
Ling 3.0 Flash VL has the larger context window (131,072 tokens vs 32,768 tokens).
Choose Ling 3.0 Flash VL if…
- • lower blended API cost matters for your workload
- • you work with longer documents, transcripts, or codebases
- • you need a model with explicit reasoning support
- • you need image inputs
Choose Qwen2.5 Coder 32B Instruct if…
- • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | Ling 3.0 Flash VL | Qwen2.5 Coder 32B Instruct |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | 9.0% SWE-bench |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | — |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | — | — |
Pricing, capabilities, and model facts
| Feature | Ling 3.0 Flash VL | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Context & model facts | ||
| Developer | inclusionAI | Qwen |
| API provider | inclusionAI | Qwen2.5 Coder 32B Instruct |
| Input context | 131,072 tokens | 32,768 tokens |
| Maximum output | 32,768 tokens | 29,491 tokens |
| Released | — | — |
| Added to Writingmate | Sep 10, 2026 | Nov 11, 2024 |
| License | Not available | Not available |
| Knowledge cutoff | — | 2024-06-30 |
| Capabilities | ||
| Inputs | Text, Image, Video | Text |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | Yes | No |
| Reasoning | Yes | No |
| Vision | Yes | No |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $0.06 | $0.66 |
| Output (per 1M tokens) | $0.18 | $1.00 |
| Blended 3:1 input/output | $0.09 | $0.745 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Sep 10, 2026.
Why Pay for Multiple Subscriptions?
Comparing Ling 3.0 Flash VL from inclusionAI with Qwen2.5 Coder 32B Instruct from Qwen? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Ling 3.0 Flash VL | Qwen2.5 Coder 32B Instruct | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Ling 3.0 Flash VL vs Qwen2.5 Coder 32B Instruct FAQ
Which is better, Ling 3.0 Flash VL or Qwen2.5 Coder 32B Instruct?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Ling 3.0 Flash VL is about 8.3× cheaper on a blended 3:1 input/output basis ($0.09 vs $0.745 per 1M tokens). Ling 3.0 Flash VL has the larger context window (131,072 tokens vs 32,768 tokens).
Which model is cheaper to use through an API?
Ling 3.0 Flash VL is about 8.3× cheaper on a blended 3:1 input/output basis ($0.09 vs $0.745 per 1M tokens).
Which model supports more context?
Ling 3.0 Flash VL has the larger context window (131,072 tokens vs 32,768 tokens).
Can I switch between Ling 3.0 Flash VL and Qwen2.5 Coder 32B Instruct?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
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