Llama 3.3 Nemotron Super 49B v1 vs Phi-4-mini vs Reka Flash 3
Too close to call on our weighted score (Llama 3.3 Nemotron Super 49B v1 58, Phi-4-mini 56, Reka Flash 3 50). The right pick depends on what you value most.
NVIDIALlama 3.3 Nemotron Super 49B v1
58/100- ECI—
- Price$0.15 / $0.15
- Context131K
MicrosoftPhi-4-mini
56/100- ECI—
- Price$0.075 / $0.30
- Context128K
Reka AIReka Flash 3
50/100- ECI—
- Price$0.10 / $0.20
- Context66K
Too close to call
It is close. Our weighted score puts them within 2 points (Llama 3.3 Nemotron Super 49B v1 58/100, Phi-4-mini 56/100, Reka Flash 3 50/100), so choose by what matters most for your work: Reka Flash 3 on price and Llama 3.3 Nemotron Super 49B v1 for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. 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 priceReka Flash 3Reka Flash 3 $0.125 · Phi-4-mini $0.131 · Llama 3.3 Nemotron Super 49B v1 $0.15 per 1M tokens (3:1 blend)
- Longest contextLlama 3.3 Nemotron Super 49B v1Llama 3.3 Nemotron Super 49B v1 131,072 · Phi-4-mini 128,000 · Reka Flash 3 65,536 tokens
- Widest inputsSame inputsLlama 3.3 Nemotron Super 49B v1: Text · Phi-4-mini: Text · Reka Flash 3: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama 3.3 Nemotron Super 49B v1 | Phi-4-mini | Reka Flash 3 |
|---|---|---|---|---|
| Price | 50% | 85 | 88 | 89 |
| Inputs & features | 30% | 35 | 25 | 10 |
| Context window | 20% | 25 | 24 | 12 |
| Overall | 100% | 58/100 | 56/100 | 50/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Llama 3.3 Nemotron Super 49B v1NVIDIA | Phi-4-miniMicrosoft | Reka Flash 3Reka AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | $0.075 (best) | $0.10 |
| Output | $0.15 (best) | $0.30 | $0.20 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 | $0.131 | $0.125 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Azure API | Median of 3 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 65,536 tokens |
| Max output | 131,072 tokens (best) | 4,096 tokens | 58,982 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | phi-4-mini | — |
| API providers | 1 | 2 | 3 (best) |
| Released | Mar 18, 2025 | Feb 26, 2025 | Mar 10, 2025 |
| Knowledge cutoff | — | Jun 2024 | Jan 31, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama 3.3 Nemotron Super 49B v1$1.80
- Phi-4-mini$1.35
- Reka Flash 3$1.40
Which should you choose?
Which is better: Llama 3.3 Nemotron Super 49B v1, Phi-4-mini or Reka Flash 3?
It is close. Our weighted score puts them within 2 points (Llama 3.3 Nemotron Super 49B v1 58/100, Phi-4-mini 56/100, Reka Flash 3 50/100), so choose by what matters most for your work: Reka Flash 3 on price and Llama 3.3 Nemotron Super 49B v1 for long inputs. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Llama 3.3 Nemotron Super 49B v1, Phi-4-mini or Reka Flash 3?
Reka Flash 3 is cheaper at $0.10 input / $0.20 output per million tokens (median across 3 API providers). Phi-4-mini costs $0.075 input / $0.30 output per million tokens (official Azure API price); Llama 3.3 Nemotron Super 49B v1 costs $0.15 input / $0.15 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.125 per million tokens for Reka Flash 3 versus $0.131 for Phi-4-mini (1× as much) and $0.15 for Llama 3.3 Nemotron Super 49B v1 (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama 3.3 Nemotron Super 49B v1 has not been scored yet, Phi-4-mini has not been scored yet and Reka Flash 3 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama 3.3 Nemotron Super 49B v1, Phi-4-mini and Reka Flash 3 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Reka Flash 3 does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama 3.3 Nemotron Super 49B v1 has the largest context window at 131,072 tokens, against 128,000 for Phi-4-mini and 65,536 for Reka Flash 3. Maximum output per response: Llama 3.3 Nemotron Super 49B v1 up to 131,072, Phi-4-mini up to 4,096, Reka Flash 3 up to 58,982 tokens.
Which can read images, PDFs, audio or video?
Llama 3.3 Nemotron Super 49B v1 accepts text; Phi-4-mini accepts text; Reka Flash 3 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Llama 3.3 Nemotron Super 49B v1 is the newest, released Mar 18, 2025. Reka Flash 3 came out Mar 10, 2025; Phi-4-mini came out Feb 26, 2025. Knowledge cutoff: Phi-4-mini Jun 2024, Reka Flash 3 Jan 31, 2025.
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 Nov 2, 2026.
