ZMIME
Comparison · 3 models · Updated Oct 4, 2026

Mistral Nemotron vs Llama 3.3 Nemotron Super 49B v1.5 vs GLM-4.5-Flash

GLM-4.5-Flash comes out ahead, 65 to 62 and 50 on our weighted score.

  1. NVIDIA

    Mistral Nemotron

    Released Jun 11, 2025Deprecated

    62/100
    • ECI—
    • PriceFree / Free
    • Context128K
  2. NVIDIA

    Llama 3.3 Nemotron Super 49B v1.5

    Released Jul 25, 2025Deprecated

    50/100
    • ECI—
    • Price$0.40 / $0.40
    • Context131K
  3. Our pick

    Z.ai (Zhipu)

    GLM-4.5-Flash

    Released Jul 28, 2025

    65/100
    • ECI—
    • PriceFree / Free
    • Context131K
01 — Verdict

GLM-4.5-Flash is our pick

GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Mistral Nemotron (62) and Llama 3.3 Nemotron Super 49B v1.5 (50). 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 priceMistral Nemotron and GLM-4.5-FlashMistral Nemotron Free · GLM-4.5-Flash Free · Llama 3.3 Nemotron Super 49B v1.5 $0.40 per 1M tokens (3:1 blend)
  • Longest contextLlama 3.3 Nemotron Super 49B v1.5 and GLM-4.5-FlashLlama 3.3 Nemotron Super 49B v1.5 131,072 · GLM-4.5-Flash 131,072 · Mistral Nemotron 128,000 tokens
  • Widest inputsSame inputsMistral Nemotron: Text · Llama 3.3 Nemotron Super 49B v1.5: Text · GLM-4.5-Flash: Text
  • Self-hostingMistral Nemotron and Llama 3.3 Nemotron Super 49B v1.5Publishes downloadable weights
How the score is built
MeasureWeightMistral NemotronLlama 3.3 Nemotron Super 49B v1.5GLM-4.5-Flash
Price50%10069100
Inputs & features30%253535
Context window20%242424
Overall100%62/10050/10065/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Mistral Nemotron vs Llama 3.3 Nemotron Super 49B v1.5 vs GLM-4.5-Flash specifications side by side
SpecificationMistral NemotronNVIDIALlama 3.3 Nemotron Super 49B v1.5NVIDIAGLM-4.5-FlashZ.ai (Zhipu)
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
InputFree (best)$0.40Free (best)
OutputFree (best)$0.40Free (best)
Cached input———
Blended (3:1)Free (best)$0.40Free (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Nvidia APIMedian of 1 providersOfficial Z.AI API
Limits
Context window128,000 tokens131,072 tokens (best)131,072 tokens (best)
Max output8,192 tokens131,072 tokens (best)98,304 tokens
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenProprietary
API model IDmistralai/mistral-nemotronnvidia/llama-3.3-nemotron-super-49b-v1.5glm-4.5-flash
API providers124 (best)
ReleasedJun 11, 2025Jul 25, 2025Jul 28, 2025
Knowledge cutoff——Apr 2025
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Mistral NemotronFree
  • Llama 3.3 Nemotron Super 49B v1.5$4.80
  • GLM-4.5-FlashFree
04 — Questions

Which should you choose?

Which is better: Mistral Nemotron, Llama 3.3 Nemotron Super 49B v1.5 or GLM-4.5-Flash?

GLM-4.5-Flash is the better all-round choice, scoring 65/100 against Mistral Nemotron (62) and Llama 3.3 Nemotron Super 49B v1.5 (50). 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, Mistral Nemotron, Llama 3.3 Nemotron Super 49B v1.5 or GLM-4.5-Flash?

Mistral Nemotron is cheaper at Free input / Free output per million tokens (official Nvidia API price). GLM-4.5-Flash costs Free input / Free output per million tokens (official Z.AI API price); Llama 3.3 Nemotron Super 49B v1.5 costs $0.40 input / $0.40 output per million tokens (median across 1 API provider; free on Nvidia). Mistral Nemotron is listed as free.

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Mistral Nemotron has not been scored yet, Llama 3.3 Nemotron Super 49B v1.5 has not been scored yet and GLM-4.5-Flash has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Mistral Nemotron, Llama 3.3 Nemotron Super 49B v1.5 and GLM-4.5-Flash 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?

Llama 3.3 Nemotron Super 49B v1.5 and GLM-4.5-Flash have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Mistral Nemotron. Maximum output per response: Mistral Nemotron up to 8,192, Llama 3.3 Nemotron Super 49B v1.5 up to 131,072, GLM-4.5-Flash up to 98,304 tokens.

Which can read images, PDFs, audio or video?

Mistral Nemotron accepts text; Llama 3.3 Nemotron Super 49B v1.5 accepts text; GLM-4.5-Flash accepts text. They handle the same number of input types.

Are any of these open source?

Mistral Nemotron and Llama 3.3 Nemotron Super 49B v1.5 publishes its weights and can be self-hosted; GLM-4.5-Flash is proprietary.

Which is newer?

GLM-4.5-Flash is the newest, released Jul 28, 2025. Llama 3.3 Nemotron Super 49B v1.5 came out Jul 25, 2025; Mistral Nemotron came out Jun 11, 2025. Knowledge cutoff: GLM-4.5-Flash Apr 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 Oct 4, 2026.