MiniMax M3 vs Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B
MiniMax M3 (DeepInfra, 524,288-token context) versus Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B (W&B Inference, 1,049,000-token context). Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B is cheaper by 35% on a blended token mix. Use the live calculator below to plug your real usage shape into both, then route the winner via Agent Command Center for shadow A/B without code changes.
Bottom line — MiniMax M3 vs Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B
MiniMax M3 and Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B target overlapping workloads but differ sharply on economics. Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B runs roughly 35% cheaper on a blended input-plus-output token mix, which translates to approximately $600 per month at mid-market volume (100K requests/day). The gap compounds at enterprise scale, making the cost axis the first filter most teams apply when deciding between these two models.
Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B ships a 1,049,000-token context window, 2.0x larger than MiniMax M3's 524,288 tokens. That headroom matters for long-document RAG pipelines, multi-turn agent sessions that accumulate tool-call history, and codebases where the entire repository needs to fit in a single prompt. If your average prompt stays under 524,288 tokens, the extra context on Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B is insurance you may never use — and MiniMax M3 may win on other axes.
For teams evaluating both models, the recommended path is a shadow A/B test: route production traffic through an OpenAI-compatible gateway, mirror a percentage to the candidate model, score both responses with an automated evaluator (faithfulness, tool-call correctness, latency), and compare cohort-level metrics over two weeks. Future AGI Agent Command Center supports this pattern with a single `base_url` change and built-in evaluators from the ai-evaluation SDK.
Live workload comparison
Same workload run through both models. The cheaper one is highlighted.
strategy: cost-optimized
primary:
model: nvidia-nvidia-nemotron-3-5-super-vl-120b-a12b
provider: wandb-inference
fallback:
model: minimaxai-minimax-m3
provider: deepinfra
shadow: { sample_rate: 0.05 } # mirror 5% of traffic to compare quality live| MiniMax M3 | Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B | |
|---|---|---|
| Input price | $0.280/M | $0.150/M |
| Output price | $1.10/M | $0.750/M |
| Context window | 524,288 | 1,049,000 |
| Max output | — | — |
| Function calling | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio input | — | — |
| Reasoning | ✓ | ✓ |
| Prompt caching | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Pricing verified | Oct 22, 2026 | Oct 22, 2026 |
Cost at scale: monthly spend at three usage volumes
Estimated monthly cost assuming 1,000 input + 200 output tokens per request — a realistic chat-agent shape. Adjust your own usage in the calculator at the top of this page for an exact number.
| Scale | MiniMax M3 | Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B | Delta |
|---|---|---|---|
| Startup 10K requests/day | $150 /mo | $90.00 /mo | $60.00/mo |
| Mid-market 100K requests/day | $1,500 /mo | $900 /mo | $600/mo |
| Enterprise 1M requests/day | $15,000 /mo | $9,000 /mo | $6,000/mo |
At enterprise scale (1M requests/day), a difference of even ~10% in unit price compounds into thousands of dollars per month. Cached input pricing and batch tiers can shift this further — both are surfaced on each model's own page.
When to choose which
Picked from the data above — not vendor marketing. Match the rules to your workload, not the other way around.
You're cost-sensitive at scale — Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B runs ~35% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume.
Your workload needs long context — Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B fits 1,049,000 tokens versus the other model's 524,288, enough headroom for full books, large codebases, or 100+ page documents in one shot.
Migration considerations
Concrete differences to wire through your stack before you flip traffic from one to the other.
- Context window changes up 100% when moving from MiniMax M3 (524,288) to Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B (1,049,000). Re-check any prompt that relies on cramming long history or documents.
- Provider changes from DeepInfra to W&B Inference. API authentication, rate-limit policy, regional availability, and billing all shift. Most teams route through an OpenAI-compatible gateway (e.g., Future AGI Agent Command Center) so the swap is a single `base_url` change instead of an SDK rewrite.
How to A/B test MiniMax M3 vs Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B in production
If you're stuck between the two, run them side-by-side on real traffic. Four steps the Future AGI team uses internally:
- 1. Point your existing OpenAI SDK at
https://gateway.futureagi.com/v1. No code change beyondbase_urland a virtual key. - 2. Mark MiniMax M3 primary, mirror 20% of traffic to Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B in shadow mode. Both responses are logged; only the primary is served to users.
- 3. Score every shadow response with an evaluator — faithfulness, tool-call correctness, response latency, cost. Built-in evaluators in ai-evaluation cover the common axes.
- 4. Compare cohort-level metrics after two weeks. Switch primary when the candidate wins on what matters to your workload — and stays within your latency budget.
Full walkthrough on the Agent Command Center page.
FAQ — MiniMax M3 vs Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B
Which is cheaper, MiniMax M3 or Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B? ▾
Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B is cheaper by roughly 35% on a blended input + output token mix. Input prices are $0.280/M for MiniMax M3 versus $0.150/M for Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B; output prices are $1.10/M versus $0.750/M. The exact savings depend on your input:output ratio — use the live calculator above to plug in your own request shape.
What is the context window of MiniMax M3 versus Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B? ▾
MiniMax M3 supports up to 524,288 tokens of context. Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B supports up to 1,049,000 tokens. Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B has the larger window by a factor of 2.0x, which matters for long-document RAG, multi-turn agent sessions, and tasks that need to keep an entire codebase in working memory.
Do MiniMax M3 and Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B both support tool calling? ▾
Yes — both MiniMax M3 and Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B support native function calling. Both also support structured output via JSON schema, so an agent can be ported between them with the same tool definitions.
Which model supports prompt caching for cost reduction? ▾
Both MiniMax M3 and Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B support prompt caching. Cached input tokens are typically discounted 50–90% versus uncached input, depending on the provider. For agents with a stable system prompt + retrieval context, the cached pricing tier is the real unit economics number to track.
When should I choose MiniMax M3 over Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B? ▾
On the data this page surfaces, MiniMax M3 is the right pick when Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B's lower price or different capability profile aren't a fit for your workload. Run the live calculator above against your actual usage shape to confirm.
When should I choose Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B over MiniMax M3? ▾
You're cost-sensitive at scale — Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B runs ~35% cheaper on a blended in+out token mix, compounding into thousands of dollars per month at production volume. Your workload needs long context — Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B fits 1,049,000 tokens versus the other model's 524,288, enough headroom for full books, large codebases, or 100+ page documents in one shot.
How do I A/B test MiniMax M3 against Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B in production? ▾
Route both through an OpenAI-compatible gateway like Future AGI Agent Command Center with shadow mode enabled. Send 100% of traffic to your primary model, mirror 10–20% to the candidate, score every response with an evaluator (faithfulness, tool-call correctness, response time), and compare cohort-level metrics for two weeks. Switch when the candidate wins on the metrics that matter to your workload and stays within your latency budget.