MAI-Code-1.1-Flash vs Qwen3.8 2.4T A95B vs Nemotron 3.5 Lightning 30B A3B
MAI-Code-1.1-Flash comes out ahead, 56 to 42 and 42 on our weighted score.
- Our pick
MicrosoftMAI-Code-1.1-Flash
56/100- ECI—
- Price—
- Context256K
Alibaba (Qwen)Qwen3.8 2.4T A95B
42/100- ECI—
- Price$2.00 / $6.00
- Context262K
NVIDIANemotron 3.5 Lightning 30B A3B
42/100- ECI—
- Price$0.05 / $0.20
- Context262K
MAI-Code-1.1-Flash is our pick
MAI-Code-1.1-Flash is the better all-round choice, scoring 56/100 against Qwen3.8 2.4T A95B (42) and Nemotron 3.5 Lightning 30B A3B (42). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceNemotron 3.5 Lightning 30B A3BNemotron 3.5 Lightning 30B A3B $0.087 · Qwen3.8 2.4T A95B $3.00 per 1M tokens (3:1 blend) · MAI-Code-1.1-Flash unpriced
- Longest contextQwen3.8 2.4T A95B and Nemotron 3.5 Lightning 30B A3BQwen3.8 2.4T A95B 262,144 · Nemotron 3.5 Lightning 30B A3B 262,144 · MAI-Code-1.1-Flash 256,000 tokens
- Widest inputsMAI-Code-1.1-FlashMAI-Code-1.1-Flash: Text, Images · Qwen3.8 2.4T A95B: Text · Nemotron 3.5 Lightning 30B A3B: Text
- Self-hostingQwen3.8 2.4T A95B and Nemotron 3.5 Lightning 30B A3BPublishes downloadable weights (qwen3.8-max)
| Measure | Weight | MAI-Code-1.1-Flash | Qwen3.8 2.4T A95B | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 45 | 45 |
| Context window | 40% | 36 | 37 | 37 |
| Overall | 100% | 56/100 | 42/100 | 42/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MAI-Code-1.1-FlashMicrosoft | Qwen3.8 2.4T A95BAlibaba (Qwen) | Nemotron 3.5 Lightning 30B A3BNVIDIA |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | — | $2.00 | $0.05 (best) |
| Output | — | $6.00 | $0.20 (best) |
| Cached input | — | — | — |
| Blended (3:1) | — | $3.00 | $0.087 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 21 providers | Median of 9 providers |
| Limits | |||
| Context window | 256,000 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 128,000 tokens | 131,072 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Openqwen3.8-max | Open |
| API model ID | — | — | nvidia/nemotron-3.5-lightning-30b-a3b |
| API providers | — | 21 (best) | 12 |
| Released | Aug 11, 2026 | Aug 12, 2026 | Aug 11, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MAI-Code-1.1-Flash—
- Qwen3.8 2.4T A95B$32.00
- Nemotron 3.5 Lightning 30B A3B$0.90
Which should you choose?
Which is better: MAI-Code-1.1-Flash, Qwen3.8 2.4T A95B or Nemotron 3.5 Lightning 30B A3B?
MAI-Code-1.1-Flash is the better all-round choice, scoring 56/100 against Qwen3.8 2.4T A95B (42) and Nemotron 3.5 Lightning 30B A3B (42). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, MAI-Code-1.1-Flash, Qwen3.8 2.4T A95B or Nemotron 3.5 Lightning 30B A3B?
Nemotron 3.5 Lightning 30B A3B is cheaper at $0.05 input / $0.20 output per million tokens (median across 9 API providers; free on Nvidia). Qwen3.8 2.4T A95B costs $2.00 input / $6.00 output per million tokens (median across 21 API providers). At a typical mix of three input tokens to one output token, that is $0.087 per million tokens for Nemotron 3.5 Lightning 30B A3B versus $3.00 for Qwen3.8 2.4T A95B (34× as much). MAI-Code-1.1-Flash has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MAI-Code-1.1-Flash has not been scored yet, Qwen3.8 2.4T A95B has not been scored yet and Nemotron 3.5 Lightning 30B A3B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MAI-Code-1.1-Flash, Qwen3.8 2.4T A95B and Nemotron 3.5 Lightning 30B A3B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.8 2.4T A95B and Nemotron 3.5 Lightning 30B A3B have the largest context windows (262,144 and 262,144 tokens), against 256,000 for MAI-Code-1.1-Flash. Maximum output per response: MAI-Code-1.1-Flash up to 128,000, Qwen3.8 2.4T A95B up to 131,072, Nemotron 3.5 Lightning 30B A3B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
MAI-Code-1.1-Flash accepts text and images; Qwen3.8 2.4T A95B accepts text; Nemotron 3.5 Lightning 30B A3B accepts text. MAI-Code-1.1-Flash handles the widest range of inputs.
Are any of these open source?
Qwen3.8 2.4T A95B and Nemotron 3.5 Lightning 30B A3B publishes its weights (qwen3.8-max) and can be self-hosted; MAI-Code-1.1-Flash is proprietary.
Which is newer?
Qwen3.8 2.4T A95B is the newest, released Aug 12, 2026. MAI-Code-1.1-Flash came out Aug 11, 2026; Nemotron 3.5 Lightning 30B A3B came out Aug 11, 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
