Which Ollama Cloud Model is Best? FIFA World Cup 2026 SVG Comparison (13 Models)
The FIFA World Cup 2026 is the biggest sporting event of the year – hosted across three nations (USA, Canada, and Mexico) for the first time in history. So we asked 13 Ollama cloud models to draw “FIFA Football World Cup 2026” as an SVG. This prompt tests whether models can combine text rendering (FIFA, World Cup, 2026), brand recognition (the iconic trophy), sports imagery (soccer ball), and national identity (flags) into a single cohesive poster.
The prompt was: Make svg image of Fifa football worldcup 2026
This is the eighth in our SVG benchmark series. See also: duck on a bicycle, duck with a parachute, duck driving a jeep, cherry blossom trees, duck programmer debugging at 3am, baby shark fish, and octopus playing chess.
Why the FIFA World Cup 2026? This prompt is uniquely challenging because it combines: (1) Text rendering – models must render “FIFA”, “World Cup”, and “2026” as actual <text> elements, which many LLMs struggle with (text in SVG is harder than shapes), (2) Brand recognition – the FIFA World Cup trophy has a very specific silhouette (a golden globe held by two athletes) that the model must recall from training data, (3) Sports imagery – a soccer ball with its iconic black-and-white pentagon/hexagon pattern, (4) National identity – the 2026 World Cup is hosted by USA, Canada, and Mexico, so flags and host nation references test the model’s knowledge of current events, (5) Poster composition – a World Cup poster needs a central focal point (trophy or ball), supporting text, and decorative elements (stars, flags, gradient backgrounds), (6) Trending relevance – the 2026 World Cup is happening right now, so models trained on recent data should know the official slogan “We Are 26” and the host nations.
The goal is not to declare a winner – it is to give you the data so you can pick the best model for your own use case. We show you the SVG, the stats, and a short analysis for each. You decide.
How to Choose the Best Ollama Model for Branded SVG Posters
The FIFA World Cup 2026 prompt rewards different things than previous prompts. Here are the criteria to use:
- Text accuracy: Does the SVG render “FIFA”, “World Cup”, and “2026” as readable text? Text rendering in SVG is hard for LLMs because it requires
<text>elements with proper positioning, fonts, and sizing. A model that draws a great shark may fail here because it cannot spell “FIFA”. - Trophy recognition: Does the SVG include a recognizable FIFA World Cup trophy? The trophy has a specific shape (golden globe on a base, held by two athletes). A model with good brand knowledge will reproduce this silhouette.
- Soccer ball: Does the SVG include a soccer ball with the iconic black-and-white pentagon/hexagon pattern? Or is it just a generic circle?
- Host nations: Does the SVG reference USA, Canada, and Mexico (the 2026 hosts)? This tests whether the model knows current events. Look for flags, country names, or colors.
- Poster composition: Is there a clear focal point (trophy, ball, or “2026” text)? Are supporting elements balanced around it? A good poster has visual hierarchy.
- Color palette: Does the SVG use a vibrant, celebratory palette (gold for the trophy, green for the field, national colors for flags)? Or is it dull and monochrome?
- SVG code quality: Does it use
<defs>,<symbol>,<use>, and gradients? Better code is easier to tweak (e.g., to recolor for a different team or add a sponsor logo).
How It Works
The script discovers all cloud-hosted models via the Ollama API (/api/tags), pulls each model, then sends the identical prompt through the OpenAI-compatible endpoint (http://localhost:11434/v1/chat/completions). Each model’s response is parsed for an <svg>...</svg> block, and the extracted SVG is saved for rendering with minimal post-processing (adding width="100%" height="auto" for responsive embedding and fixing XML errors so the SVG renders in browsers).
Cloud models are identified by the remote_host field in the API response – these models are hosted on Ollama Cloud rather than running locally. This means even very large models (671B parameters) can be queried instantly without local GPU resources.
Summary Table: Compare All Models at a Glance
Use this table to quickly compare models on the metrics that matter. The verdict column is a one-line summary to help you shortlist – but read the per-model sections below for the full picture before you decide.
| # | Model | SVG Size | Shapes | Colors | Complexity | Verdict |
|---|---|---|---|---|---|---|
| 1 | deepseek-v4-flash:cloud | 11177 | 70 | 16 | Very high | Richest scene |
| 2 | deepseek-v4-pro:cloud | 7767 | 28 | 6 | High | Detailed |
| 3 | gemma4:31b-cloud | 1942 | 11 | 11 | Compact | Compact |
| 4 | gemma4:cloud | 2238 | 11 | 6 | Compact | Compact |
| 5 | glm-5.1:cloud | 21973 | 99 | 17 | Very high | Richest scene |
| 6 | glm-5.2:cloud | 5841 | 39 | 14 | High | Detailed |
| 7 | gpt-oss:120b-cloud | 2624 | 6 | 5 | Compact | Balanced |
| 8 | kimi-k2.6:cloud | 7048 | 19 | 8 | High | Detailed |
| 9 | minimax-m2.7:cloud | 4992 | 17 | 6 | Medium | Balanced |
| 10 | minimax-m3:cloud | 7812 | 58 | 17 | High | Detailed |
| 11 | nemotron-3-super:cloud | 1775 | 9 | 2 | Compact | Compact |
| 12 | nemotron-3-ultra:cloud | 7733 | 24 | 12 | High | Detailed |
| 13 | qwen3.5:397b-cloud | 3391 | 7 | 6 | Medium | Balanced |
| 14 | bjoernb/claude-opus-4-5:latest | - | - | - | - | Retired (410) |
| 15 | deepseek-v3.1:671b-cloud | - | - | - | - | Retired (410) |
| 16 | glm-5:cloud | - | - | - | - | Retired (410) |
| 17 | qwen3-vl:235b-cloud | - | - | - | - | Retired (410) |
13 out of 17 active models produced a valid SVG. The 4 retired models returned HTTP 410 Gone (removed from Ollama Cloud on 2026-07-15).
Quick Recommendation by Use Case
If you just want a shortcut, here is which model to pick based on what you care about:
- You want the most detailed World Cup poster SVG: pick models labeled “Very high” complexity in the table above
- You want the fastest response: look at the per-model sections below for the elapsed time
- You want the cleanest, most reusable SVG code: pick models that use
<defs>,<symbol>,<use>, and transforms (see raw source below each SVG) - You want a small, efficient SVG for web embedding: pick models with “Compact” verdict
- You want accurate text rendering (“FIFA”, “2026”): check the per-model analysis – text rendering is the hardest part of this prompt
- You want a balance of detail and speed: pick models labeled “Balanced” or “Detailed”
- You want to compare within a model family: pick
deepseek-v4-provsdeepseek-v4-flash, orglm-5.1vsglm-5.2, orminimax-m2.7vsminimax-m3
Now read on for the full per-model breakdown and judge for yourself.
1. deepseek-v4-flash:cloud
SVG size: 11177 characters
Complexity: Very high
Shape elements: 70
Distinct colors: 16
Raw response: 11643 characters
Generation time: 34.8s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Applies SVG filters (e.g., drop shadows, glows) for atmospheric effects
- Uses
<use>references to reuse defined elements – ideal for repeating flag patterns or stars - Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Defines reusable
<symbol>elements – shows mature SVG authoring for repeating motifs like flags - Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders the FIFA World Cup trophy – the model captured the iconic symbol of the tournament
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
- Renders flags – the model captured the international/national element of the World Cup
- Renders a stadium – the model captured the venue setting
- References “We Are 26” – the model captured the official 2026 tournament slogan
- Includes stars – the model added celebratory/tournament decoration
With 11,177 characters of SVG markup, this is a detailed output that balances richness with readability. View raw SVG source
```xml ```
2. deepseek-v4-pro:cloud
SVG size: 7767 characters
Complexity: High
Shape elements: 28
Distinct colors: 6
Raw response: 8206 characters
Generation time: 77.8s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Applies SVG filters (e.g., drop shadows, glows) for atmospheric effects
- Uses
<use>references to reuse defined elements – ideal for repeating flag patterns or stars - Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders the FIFA World Cup trophy – the model captured the iconic symbol of the tournament
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
- Includes stars – the model added celebratory/tournament decoration
With 7,767 characters of SVG markup, this is a detailed output that balances richness with readability. View raw SVG source
```xml ```
3. gemma4:31b-cloud
SVG size: 1942 characters
Complexity: Compact
Shape elements: 11
Distinct colors: 11
Raw response: 3115 characters
Generation time: 9.0s
Analysis
Notable SVG techniques used:
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
- Includes stars – the model added celebratory/tournament decoration
With 1,942 characters of SVG markup, this is a compact output – minimal but potentially efficient for web embedding. View raw SVG source
```xml ```
4. gemma4:cloud
SVG size: 2238 characters
Complexity: Compact
Shape elements: 11
Distinct colors: 6
Raw response: 3298 characters
Generation time: 10.4s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Applies SVG filters (e.g., drop shadows, glows) for atmospheric effects
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
With 2,238 characters of SVG markup, this is a balanced output – enough detail to be recognizable without being bloated. View raw SVG source
```xml ```
5. glm-5.1:cloud
SVG size: 21973 characters
Complexity: Very high
Shape elements: 99
Distinct colors: 17
Raw response: 26157 characters
Generation time: 70.1s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Applies SVG filters (e.g., drop shadows, glows) for atmospheric effects
- Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders the FIFA World Cup trophy – the model captured the iconic symbol of the tournament
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
- Renders flags – the model captured the international/national element of the World Cup
- Includes stars – the model added celebratory/tournament decoration
With over 21,973 characters of SVG markup, this is one of the most detailed outputs in the comparison. The model invested significant effort in rendering the trophy, text, flags, and decorative elements. View raw SVG source
```xml ```
6. glm-5.2:cloud
SVG size: 5841 characters
Complexity: High
Shape elements: 39
Distinct colors: 14
Raw response: 5907 characters
Generation time: 40.0s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Applies SVG filters (e.g., drop shadows, glows) for atmospheric effects
- Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders the FIFA World Cup trophy – the model captured the iconic symbol of the tournament
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
- Renders flags – the model captured the international/national element of the World Cup
- Renders a stadium – the model captured the venue setting
- Includes stars – the model added celebratory/tournament decoration
With 5,841 characters of SVG markup, this is a detailed output that balances richness with readability. View raw SVG source
```xml ```
7. gpt-oss:120b-cloud
SVG size: 2624 characters
Complexity: Compact
Shape elements: 6
Distinct colors: 5
Raw response: 4267 characters
Generation time: 17.8s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Uses
<use>references to reuse defined elements – ideal for repeating flag patterns or stars - Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
- Includes stars – the model added celebratory/tournament decoration
With 2,624 characters of SVG markup, this is a balanced output – enough detail to be recognizable without being bloated. View raw SVG source
```xml ```
8. kimi-k2.6:cloud
SVG size: 7048 characters
Complexity: High
Shape elements: 19
Distinct colors: 8
Raw response: 10560 characters
Generation time: 24.3s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Applies SVG filters (e.g., drop shadows, glows) for atmospheric effects
- Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders a soccer ball – the model captured the sport’s central object
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
- Includes stars – the model added celebratory/tournament decoration
With 7,048 characters of SVG markup, this is a detailed output that balances richness with readability. View raw SVG source
```xml ```
9. minimax-m2.7:cloud
SVG size: 4992 characters
Complexity: Medium
Shape elements: 17
Distinct colors: 6
Raw response: 5388 characters
Generation time: 41.6s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Applies SVG filters (e.g., drop shadows, glows) for atmospheric effects
- Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders the FIFA World Cup trophy – the model captured the iconic symbol of the tournament
- Includes stars – the model added celebratory/tournament decoration
With 4,992 characters of SVG markup, this is a balanced output – enough detail to be recognizable without being bloated. View raw SVG source
```xml ```
10. minimax-m3:cloud
SVG size: 7812 characters
Complexity: High
Shape elements: 58
Distinct colors: 17
Raw response: 8655 characters
Generation time: 24.8s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders the FIFA World Cup trophy – the model captured the iconic symbol of the tournament
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
- Renders flags – the model captured the international/national element of the World Cup
- Includes stars – the model added celebratory/tournament decoration
With 7,812 characters of SVG markup, this is a detailed output that balances richness with readability. View raw SVG source
```xml ```
11. nemotron-3-super:cloud
SVG size: 1775 characters
Complexity: Compact
Shape elements: 9
Distinct colors: 2
Raw response: 4137 characters
Generation time: 31.8s
Analysis
Notable SVG techniques used:
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
With 1,775 characters of SVG markup, this is a compact output – minimal but potentially efficient for web embedding. View raw SVG source
```xml ```
12. nemotron-3-ultra:cloud
SVG size: 7733 characters
Complexity: High
Shape elements: 24
Distinct colors: 12
Raw response: 9603 characters
Generation time: 46.0s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Applies SVG filters (e.g., drop shadows, glows) for atmospheric effects
- Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders “2026” – the model captured the year, critical for this specific World Cup
- Renders the FIFA World Cup trophy – the model captured the iconic symbol of the tournament
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
- Renders flags – the model captured the international/national element of the World Cup
- References “We Are 26” – the model captured the official 2026 tournament slogan
With 7,733 characters of SVG markup, this is a detailed output that balances richness with readability. View raw SVG source
```xml ```
13. qwen3.5:397b-cloud
SVG size: 3391 characters
Complexity: Medium
Shape elements: 7
Distinct colors: 6
Raw response: 3744 characters
Generation time: 37.0s
Analysis
Notable SVG techniques used:
- Uses
<defs>blocks for reusable components (a sign of well-structured, editable SVG) - Includes gradient fills for richer visual depth (great for the gold trophy and vibrant backgrounds)
- Applies SVG filters (e.g., drop shadows, glows) for atmospheric effects
- Uses transforms (translate, rotate, scale) for positioning the trophy, ball, and flags
- Renders actual text (FIFA, World Cup, 2026, host nation names) – a strong sign the model understood the prompt
- Contains XML comments, showing the model’s internal documentation of its work
Concept adherence:
- Renders the “FIFA” text – the model captured the tournament brand
- Renders “World Cup” text – the model captured the event name
- Renders a soccer ball – the model captured the sport’s central object
- References USA (host nation) – the model knew the 2026 hosts
- References Canada (host nation) – the model knew the 2026 hosts
- References Mexico (host nation) – the model knew the 2026 hosts
With 3,391 characters of SVG markup, this is a balanced output – enough detail to be recognizable without being bloated. View raw SVG source
```xml ```
Conclusion
That’s all 13 FIFA World Cup 2026 SVGs from the active Ollama cloud models. This trending-event prompt tested a different skill set than our previous prompts: text rendering (FIFA, World Cup, 2026), brand recognition (the trophy), sports imagery (soccer ball), national identity (flags of USA, Canada, Mexico), and poster composition.
No single model is best at everything. A model that produces a stunning octopus may produce a basic World Cup poster, and vice versa. The best model for you depends on your specific use case:
- For maximum detail: look at the “Very high” complexity models
- For speed: look at the generation times in each section
- For text accuracy: check which models actually rendered “FIFA”, “World Cup”, and “2026” as readable text
- For brand knowledge: check which models rendered a recognizable FIFA trophy and knew the 2026 host nations
- For code quality: look at the raw SVG source – clean, well-structured code is easier to customize
Try the same prompt yourself with Ollama Cloud and see if you agree with our analysis. And check out the other posts in our SVG benchmark series:
- Duck on a bicycle
- Duck with a parachute
- Duck driving a jeep
- Cherry blossom trees
- Duck programmer debugging at 3am
- Baby shark fish
- Octopus playing chess
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