Magnum v4 72B vs Ling 3.0 Flash VL: which should you choose in 2026?
Magnum v4 72B (by Anthracite) and Ling 3.0 Flash VL (by inclusionAI) 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 34.7× cheaper on a blended 3:1 input/output basis ($0.09 vs $3.13 per 1M tokens).
Ling 3.0 Flash VL has the larger context window (131,072 tokens vs 32,768 tokens).
Choose Magnum v4 72B if…
- • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage
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
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 | Magnum v4 72B | Ling 3.0 Flash VL |
|---|---|---|
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 | — | — |
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 | — | — |
Benchmark sources
Reviewed evidence last updated 2026-08-09.
Pricing, capabilities, and model facts
| Feature | Magnum v4 72B | Ling 3.0 Flash VL |
|---|---|---|
| Context & model facts | ||
| Developer | Anthracite | inclusionAI |
| API provider | Magnum v4 72B | inclusionAI |
| Input context | 32,768 tokens | 131,072 tokens |
| Maximum output | 4,096 tokens | 32,768 tokens |
| Released | — | — |
| Added to Writingmate | Oct 22, 2024 | Sep 10, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | 2024-06-30 | — |
| Capabilities | ||
| Inputs | Text | Text, Image, Video |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | No | Yes |
| Reasoning | No | Yes |
| Vision | No | Yes |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $2.50 | $0.06 |
| Output (per 1M tokens) | $5.00 | $0.18 |
| Blended 3:1 input/output | $3.13 | $0.09 |
| 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 Magnum v4 72B from Anthracite with Ling 3.0 Flash VL from inclusionAI? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Magnum v4 72B | Ling 3.0 Flash VL | 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 |
Magnum v4 72B vs Ling 3.0 Flash VL FAQ
Which is better, Magnum v4 72B or Ling 3.0 Flash VL?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Ling 3.0 Flash VL is about 34.7× cheaper on a blended 3:1 input/output basis ($0.09 vs $3.13 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 34.7× cheaper on a blended 3:1 input/output basis ($0.09 vs $3.13 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 Magnum v4 72B and Ling 3.0 Flash VL?
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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