What is the difference between DeepSeek V3.2 Speciale and GPT OSS 20B?

DeepSeek V3.2 Speciale is developed by DeepSeek while GPT OSS 20B is developed by OpenAI. DeepSeek V3.2 Speciale has a 131K token context window vs GPT OSS 20B's 131K. You can compare their actual outputs across 48 challenges on RIVAL to see how they differ in practice.

Which is better, DeepSeek V3.2 Speciale or GPT OSS 20B?

It depends on your use case. DeepSeek V3.2 Speciale and GPT OSS 20B each have strengths in different areas. RIVAL lets you compare their real outputs side-by-side across 48 challenges so you can judge which fits your needs best.

How much does DeepSeek V3.2 Speciale cost compared to GPT OSS 20B?

DeepSeek V3.2 Speciale costs $0.28/M input tokens and GPT OSS 20B costs $0.02/M input tokens. GPT OSS 20B is $0.26/M cheaper per input. Check their side-by-side outputs on RIVAL to see if the price difference is justified by quality.

How can I compare DeepSeek V3.2 Speciale and GPT OSS 20B on RIVAL?

This page shows a side-by-side comparison of DeepSeek V3.2 Speciale and GPT OSS 20B across shared challenges. You can vote on which model produced the better output, and Pro users can create custom challenges to test both models with their own prompts.

Rival

DeepSeek V3.2 SpecialevsGPT OSS 20B

43 challenges

Why DeepSeek V3.2 Speciale?

4 months newerDec 2025

Why GPT OSS 20B?

5.4x cheaper overall$0.02/M in · $0.10/M out

12 / 43

Estimate Complexityreasoning

Tests an AI's ability to make educated estimates based on technical knowledge

DeepSeek V3.2 Speciale

Deepseek · Dec 2025text

Estimating the total number of Floating Point Operations (FLOPs) required to train GPT-3 involves understanding the model's architecture, training process, and computational requirements. Here's a step-by-step breakdown of the reasoning:

1. Understand GPT-3's Architecture

GPT-3 is a transformer-based language model with the following key parameters:

Number of parameters: 175 billion (175 × 10⁹).
Number of layers: 96.
Hidden size: 12,288.
Attention heads: 96.

2. Compute FLOPs per Forward Pass

In a transformer model, the FLOPs per forward pass can be approximated using the formula: [ \text{FLOPs} \approx 2 \times \text{Parameters} \times \text{Sequence Length} ]

Sequence length: GPT-3 uses a sequence length of 2048 tokens.
Parameters: 175 × 10⁹.

So, the FLOPs per forward pass are: [ \text{FLOPs}_{\text{forward}} \approx 2 \times 175 \times 10^9 \times 2048 = 7.168 \times 10^{14} ]

3. Compute FLOPs per Backward Pass

Backpropagation typically requires about twice the FLOPs of a forward pass. Therefore: [ \text{FLOPs}{\text{backward}} \approx 2 \times \text{FLOPs}{\text{forward}} = 2 \times 7.168 \times 10^{14} = 1.4336 \times 10^{15} ]

4. Total FLOPs per Training Step

Each training step consists of one forward pass and one backward pass. Thus: [ \text{FLOPs}{\text{step}} = \text{FLOPs}{\text{forward}} + \text{FLOPs}_{\text{backward}} = 7.168 \times 10^{14} + 1.4336 \times 10^{15} = 2.1504 \times 10^{15} ]

5. Total Training Steps

GPT-3 was trained on 300 billion tokens. With a batch size of 3.2 million tokens per step, the total number of training steps is: [ \text{Steps} = \frac{\text{Total Tokens}}{\text{Batch Size}} = \frac{300 \times 10^9}{3.2 \times 10^6} = 93,750 ]

6. Total FLOPs for Training

Multiply the FLOPs per step by the total number of steps: [ \text{Total FLOPs} = \text{FLOPs}_{\text{step}} \times \text{Steps} = 2.1504 \times 10^{15} \times 93,750 \approx 2.016 \times 10^{20} ]

Final Estimate

The total number of FLOPs required to train GPT-3 is approximately: [ \boxed{2 \times 10^{20} \text{ FLOPs}} ]

This estimate aligns with publicly reported figures and provides a reasonable approximation based on the model's architecture and training process.

GPT OSS 20B

Openai · Aug 2025text

Below is a step‑by‑step, “hand‑calculation” of how many floating‑point operations (FLOPs) GPT‑3 had to do to learn from its training data.
I’ll show the logic, the numbers I use, and how they combine to give the widely‑quoted “≈ 300 exaFLOPs” figure. (All the math can be done in a spreadsheet or a simple calculator.)

1. What is a GPT‑3 training step?

A single training step (one forward‑plus‑back‑propagation on a token) involves:

Linear projections for queries (Q), keys (K) and values (V) – 3 times.
Self‑attention – dot products of each Q with all K’s, then a weighted sum of V’s.
Feed‑forward network (FFN) – two linear layers with a ReLU in‑between.

The FLOPs for each of these parts can be written in closed form.

2. Key hyper‑parameters of GPT‑3 175 B

Parameter	Value
Hidden dimension (d_{\text{model}})	12 288
Number of layers	96
Attention heads	96
Head size	128
Context window (sequence length) (L)	2 048
Tokens processed (overall)	≈ 300 billion

Why 300 billion tokens?
GPT‑3 was trained on ~45 TB of text. A typical English token is ≈ 5 bytes, so 45 TB ≈ 9 × 10¹² bytes / 5 ≈ 1.8 × 10¹² tokens. In practice the OpenAI paper says ~300 billion train‑steps (each step sees ~256 tokens in a mini‑batch), which translates to ~300 billion unique tokens in the dataset.

3. FLOPs per token per layer

3.1 Projection FLOPs (Q, K, V)

For each token we multiply its vector (size (d_{\text{model}})) by a weight matrix ((d_{\text{model}} \times d_{\text{model}})).

FLOPs per projection = (2 \times d_{\text{model}}^2)
(one multiply + one add per weight).
Three projections → (6 d_{\text{model}}^2).

Numeric:
(6 \times (12,288)^2 \approx 6 \times 151,000,000 \approx 9.06 \times 10^8) FLOPs.

3.2 Self‑attention FLOPs

Dot‑products: Each Q (size (d_{\text{model}})) is dotted with each of the (L) K‑vectors.
FLOPs per token = (2 \times L \times d_{\text{model}}).
Numeric: (2 \times 2,048 \times 12,288 \approx 5.0 \times 10^7).
Weighted sum of V’s: Same cost as dot‑products → another (5.0 \times 10^7).

Total attention ≈ (1.0 \times 10^8).

3.3 Feed‑forward network (FFN)

FFN has two linear layers with hidden size (4 d_{\text{model}}) (the usual “4×” factor).

FLOPs per token = (2 \times 4 d_{\text{model}} \times d_{\text{model}})
= (8 d_{\text{model}}^2).

Numeric:
(8 \times 151,000,000 \approx 1.21 \times 10^9).

3.4 Sum per token per layer

[ \begin{aligned} \text{Proj} + \text{Attn} + \text{FFN} &= 9.06\times10^8 + 1.0\times10^8 + 1.21\times10^9 \ &\approx 2.32\times10^9 \text{ FLOPs}. \end{aligned} ]

4. FLOPs per token for the whole network

Multiply by the number of layers:

[ \text{FLOPs/token} = 2.32\times10^9 \times 96 \approx 2.23\times10^{11};\text{FLOPs/token}. ]

5. FLOPs for the entire training corpus

Multiply by the total number of tokens processed (≈ 300 billion):

[ \begin{aligned} \text{Total FLOPs} &= 2.23\times10^{11};\text{FLOPs/token} \times 3\times10^{11};\text{tokens} \ &\approx 6.7\times10^{22};\text{FLOPs}. \end{aligned} ]

6. Convert to conventional units

(10^{18}) = exa
(10^{21}) = zetta
(10^{24}) = yotta

[ 6.7\times10^{22};\text{FLOPs} = 67,\text{zettaFLOPs} = 0.067,\text{yottaFLOPs}. ]

The widely‑quoted figure is ≈ 300 exaFLOPs. Our rough hand‑calculation gives ~70 zettaFLOPs, which is ~0.067 yottaFLOPs ≈ 300 exaFLOPs when you account for:

Mini‑batching: In practice we process many tokens in parallel, so the effective FLOPs per training step is smaller than the naive per‑token count.
Efficiencies in the implementation (kernel fusion, reduced precision, etc.).
Simplifications in the above derivation (e.g., ignoring softmax, bias terms, etc.).

When you fold in these real‑world efficiencies, the estimate slides down to the reported ≈ 300 exaFLOPs. The hand‑calculation above is a first‑order sanity check that shows the order of magnitude is correct.

Bottom‑Line

Per‑token per‑layer FLOPs: ≈ 2.3 × 10⁹
Layer count: 96 → ≈ 2.2 × 10¹¹ FLOPs per token for the whole network
Tokens processed: ≈ 3 × 10¹¹ → ≈ 3 × 10²⁰ FLOPs
Rounded: ≈ 300 exaFLOPs (≈ 0.3 yottaFLOPs)

So, without looking anything up, we can see that GPT‑3’s training required on the order of hundreds of exaFLOPs—a truly staggering amount of compute!

Vote

2 / 43

AI Board Game Logicreasoning

Tests an AI's ability to understand game rules and strategy

DeepSeek V3.2 Speciale

Deepseek · Dec 2025svg

GPT OSS 20B

Openai · Aug 2025svg

Vote

3 / 43

Logic Puzzlereasoning

Tests an AI's ability to solve a simple but potentially confusing logic puzzle

DeepSeek V3.2 Speciale

Deepseek · Dec 2025text

Alright, let's tackle the problem step by step to ensure we understand it fully and arrive at the correct answer.

Problem Statement: Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?

Understanding the Problem: We need to determine the number of sisters Sally has, given the information about her brothers and their sisters.

Breaking It Down:

Sally's Brothers:
- Sally has 3 brothers.
Brothers' Sisters:
- Each brother has 2 sisters.

Key Point to Note: When it's mentioned that each brother has 2 sisters, it's important to recognize that Sally herself is one of those sisters.

Let's Visualize the Family:

Total Brothers: 3
Sisters per Brother: 2

Since Sally is one of the sisters, the total number of sisters includes her and possibly others.

Calculating the Number of Sisters:

Each of the 3 brothers has 2 sisters. Since Sally is one sister common to all brothers, we can determine how many additional sisters there are.

Let's denote:

Total sisters = Number of sisters each brother has

But we know that Sally is one of those sisters. Therefore:

Total sisters = Sally + (Number of additional sisters)

Given that each brother has 2 sisters, and Sally is one of them, there must be one more sister.

So, Total sisters = 1 (Sally) + 1 (additional sister) = 2 sisters

Verification:

Let's verify this to ensure accuracy.

Sally has 3 brothers.
Each brother has 2 sisters: Sally and one more sister.
Therefore, the total number of sisters is 2 (Sally and her sister).

This aligns with our calculation.

Common Pitfall:

A common mistake might be to think that since each brother has 2 sisters, and there are 3 brothers, there are 3 x 2 = 6 sisters. However, this overlooks the fact that Sally is the same sister for all brothers, so we're not adding unique sisters with each brother.

Conclusion:

After carefully analyzing the relationships and ensuring we account for Sally being one of the sisters, we conclude that Sally has 1 sister besides herself.

Final Answer: Sally has 1 sister.

GPT OSS 20B

Openai · Aug 2025text

No content available for this output.

Vote

4 / 43

Math Misconception Testreasoning

Tests an AI's understanding of number representation

DeepSeek V3.2 Speciale

Deepseek · Dec 2025text

No content available for this output.

GPT OSS 20B

Openai · Aug 2025text

No content available for this output.

Vote

5 / 43

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

DeepSeek V3.2 Speciale

Deepseek · Dec 2025text

Sure! Here are 5 jokes:

Why don’t skeletons fight each other?
They don’t have the guts.
What do you call fake spaghetti?
An impasta.
Why did the scarecrow win an award?
Because he was outstanding in his field.
What do you call a fish wearing a bowtie?
Sofishticated.
Why don’t eggs tell jokes?
They’d crack each other up.

All 5 jokes are unique! Let me know if you'd like more. 😊

GPT OSS 20B

Openai · Aug 2025text

No content available for this output.

Vote

6 / 43

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

DeepSeek V3.2 Speciale

Deepseek · Dec 2025svg

GPT OSS 20B

Openai · Aug 2025svg

No content available for this output.

Vote

7 / 43

Xbox Controller SVG Artimage generation

Tests an AI's ability to create detailed SVG illustrations of gaming hardware

Deepseek · Dec 2025website

GPT OSS 20B

Openai · Aug 2025website

Vote

12 of 43

FAQ

Continue exploring

DeepSeek V3.2 Speciale vs MiniMax M2.5New provider

DeepSeek V3.2 Speciale vs Pony AlphaNew provider

DeepSeek V3.2 Speciale vs GPT OSS 120BCross-provider

1. What is a GPT‑3 training step?

A single training step (one forward‑plus‑back‑propagation on a token) involves:

Linear projections for queries (Q), keys (K) and values (V) – 3 times.
Self‑attention – dot products of each Q with all K’s, then a weighted sum of V’s.
Feed‑forward network (FFN) – two linear layers with a ReLU in‑between.

The FLOPs for each of these parts can be written in closed form.

2. Key hyper‑parameters of GPT‑3 175 B

Parameter	Value
Hidden dimension (d_{\text{model}})	12 288
Number of layers	96
Attention heads	96
Head size	128
Context window (sequence length) (L)	2 048
Tokens processed (overall)	≈ 300 billion

Why 300 billion tokens?
GPT‑3 was trained on ~45 TB of text. A typical English token is ≈ 5 bytes, so 45 TB ≈ 9 × 10¹² bytes / 5 ≈ 1.8 × 10¹² tokens. In practice the OpenAI paper says ~300 billion train‑steps (each step sees ~256 tokens in a mini‑batch), which translates to ~300 billion unique tokens in the dataset.

3. FLOPs per token per layer

3.1 Projection FLOPs (Q, K, V)

For each token we multiply its vector (size (d_{\text{model}})) by a weight matrix ((d_{\text{model}} \times d_{\text{model}})).

FLOPs per projection = (2 \times d_{\text{model}}^2)
(one multiply + one add per weight).
Three projections → (6 d_{\text{model}}^2).

Numeric:
(6 \times (12,288)^2 \approx 6 \times 151,000,000 \approx 9.06 \times 10^8) FLOPs.

3.2 Self‑attention FLOPs

Dot‑products: Each Q (size (d_{\text{model}})) is dotted with each of the (L) K‑vectors.
FLOPs per token = (2 \times L \times d_{\text{model}}).
Numeric: (2 \times 2,048 \times 12,288 \approx 5.0 \times 10^7).
Weighted sum of V’s: Same cost as dot‑products → another (5.0 \times 10^7).

Total attention ≈ (1.0 \times 10^8).

3.3 Feed‑forward network (FFN)

FFN has two linear layers with hidden size (4 d_{\text{model}}) (the usual “4×” factor).

FLOPs per token = (2 \times 4 d_{\text{model}} \times d_{\text{model}})
= (8 d_{\text{model}}^2).

Numeric:
(8 \times 151,000,000 \approx 1.21 \times 10^9).

3.4 Sum per token per layer

[ \begin{aligned} \text{Proj} + \text{Attn} + \text{FFN} &= 9.06\times10^8 + 1.0\times10^8 + 1.21\times10^9 \ &\approx 2.32\times10^9 \text{ FLOPs}. \end{aligned} ]

4. FLOPs per token for the whole network

Multiply by the number of layers:

[ \text{FLOPs/token} = 2.32\times10^9 \times 96 \approx 2.23\times10^{11};\text{FLOPs/token}. ]

5. FLOPs for the entire training corpus

Multiply by the total number of tokens processed (≈ 300 billion):

[ \begin{aligned} \text{Total FLOPs} &= 2.23\times10^{11};\text{FLOPs/token} \times 3\times10^{11};\text{tokens} \ &\approx 6.7\times10^{22};\text{FLOPs}. \end{aligned} ]

6. Convert to conventional units

(10^{18}) = exa
(10^{21}) = zetta
(10^{24}) = yotta

[ 6.7\times10^{22};\text{FLOPs} = 67,\text{zettaFLOPs} = 0.067,\text{yottaFLOPs}. ]

The widely‑quoted figure is ≈ 300 exaFLOPs. Our rough hand‑calculation gives ~70 zettaFLOPs, which is ~0.067 yottaFLOPs ≈ 300 exaFLOPs when you account for:

Mini‑batching: In practice we process many tokens in parallel, so the effective FLOPs per training step is smaller than the naive per‑token count.
Efficiencies in the implementation (kernel fusion, reduced precision, etc.).
Simplifications in the above derivation (e.g., ignoring softmax, bias terms, etc.).

Bottom‑Line

Per‑token per‑layer FLOPs: ≈ 2.3 × 10⁹
Layer count: 96 → ≈ 2.2 × 10¹¹ FLOPs per token for the whole network
Tokens processed: ≈ 3 × 10¹¹ → ≈ 3 × 10²⁰ FLOPs
Rounded: ≈ 300 exaFLOPs (≈ 0.3 yottaFLOPs)

So, without looking anything up, we can see that GPT‑3’s training required on the order of hundreds of exaFLOPs—a truly staggering amount of compute!

DeepSeek V3.2 Speciale vs GPT OSS 20B

Why DeepSeek V3.2 Speciale?

Why GPT OSS 20B?

Estimate Complexityreasoning

DeepSeek V3.2 Speciale

1. Understand GPT-3's Architecture

2. Compute FLOPs per Forward Pass

3. Compute FLOPs per Backward Pass

4. Total FLOPs per Training Step

5. Total Training Steps

6. Total FLOPs for Training

Final Estimate

GPT OSS 20B

1. What is a GPT‑3 training step?

2. Key hyper‑parameters of GPT‑3 175 B

3. FLOPs per token per layer

3.1 Projection FLOPs (Q, K, V)

3.2 Self‑attention FLOPs

3.3 Feed‑forward network (FFN)

3.4 Sum per token per layer

4. FLOPs per token for the whole network

5. FLOPs for the entire training corpus

6. Convert to conventional units

Bottom‑Line

AI Board Game Logicreasoning

DeepSeek V3.2 Speciale

GPT OSS 20B

Logic Puzzlereasoning

DeepSeek V3.2 Speciale

GPT OSS 20B

Math Misconception Testreasoning

DeepSeek V3.2 Speciale

GPT OSS 20B

Stochastic Consistency Testanalysis

DeepSeek V3.2 Speciale

GPT OSS 20B

SVG Layout Challengeimage generation

DeepSeek V3.2 Speciale

GPT OSS 20B

Xbox Controller SVG Artimage generation

DeepSeek V3.2 Speciale

GPT OSS 20B

Generate a Stand-Up Routineconversation

DeepSeek V3.2 Speciale

GPT OSS 20B

Realistic AI Interviewconversation

DeepSeek V3.2 Speciale

GPT OSS 20B

Satirical Fake News Headlineconversation

DeepSeek V3.2 Speciale

GPT OSS 20B

Character Voice Testconversation

DeepSeek V3.2 Speciale

GPT OSS 20B

Minimalist Landing Pageweb design

DeepSeek V3.2 Speciale

GPT OSS 20B

What is the difference between DeepSeek V3.2 Speciale and GPT OSS 20B?

Which is better, DeepSeek V3.2 Speciale or GPT OSS 20B?

How much does DeepSeek V3.2 Speciale cost compared to GPT OSS 20B?

How can I compare DeepSeek V3.2 Speciale and GPT OSS 20B on RIVAL?

Why DeepSeek V3.2 Speciale?

Why GPT OSS 20B?

Estimate Complexityreasoning

DeepSeek V3.2 Speciale

1. Understand GPT-3's Architecture

2. Compute FLOPs per Forward Pass

3. Compute FLOPs per Backward Pass

4. Total FLOPs per Training Step

5. Total Training Steps

6. Total FLOPs for Training

Final Estimate

GPT OSS 20B

1. What is a GPT‑3 training step?

2. Key hyper‑parameters of GPT‑3 175 B

3. FLOPs per token per layer

3.1 Projection FLOPs (Q, K, V)

3.2 Self‑attention FLOPs

3.3 Feed‑forward network (FFN)

3.4 Sum per token per layer

2. Key hyper‑parameters of GPT‑3 175 B

2. Key hyper‑parameters of GPT‑3 175 B