State of Multimodal Foundation Models — Early 2026

Comprehensive Comparison Report

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1. Model Overview

OpenAI: GPT-4o and GPT-4.5

GPT-4o (released May 2024) was OpenAI’s flagship omnimodal model, natively processing text, images, and audio in a single architecture. It represented a major shift from the pipeline approach (separate ASR/TTS modules) to end-to-end multimodal reasoning.

• Text: Top-tier reasoning and instruction following. Strong performance on MMLU (~88%), GPQA, and HumanEval.
• Image understanding: Accepts images natively. Strong on visual QA, chart reading, OCR, and spatial reasoning. Competitive on MathVista and MMMU benchmarks.
• Image generation: GPT-4o gained native image generation capabilities in early 2025, producing images directly within the model rather than routing to DALL-E. This was notable for its instruction-following fidelity and text rendering in images.
• Audio/Speech: Native speech-in, speech-out. Real-time conversational voice with emotion, laughter, singing. Latency on par with human conversation (~320ms average).
• Video understanding: Limited. Could process video frames as image sequences but lacked native video understanding at the architectural level.
• Code: Strong. Competitive with specialized coding models on SWE-bench and LiveCodeBench.
• Tool use: Robust function calling, structured outputs, and multi-tool orchestration.

GPT-4.5 (released February 2025) took a different approach — it was OpenAI’s largest and most expensive model, focused on unsupervised learning breadth rather than chain-of-thought reasoning. It showed improvements in EQ, “worldliness,” and reduced hallucination rates but was not a multimodal leap over GPT-4o.

GPT-o1, o3, o4-mini (reasoning model family): These models focused on chain-of-thought and deep reasoning. o3 and o4-mini (released April 2025) added tool use, image understanding, and code execution capabilities to the reasoning model line, making them strong for complex analytical tasks involving visual inputs.

Pricing (GPT-4o as of early 2025):
- Input: $2.50 / 1M tokens; Output: $10.00 / 1M tokens
- Image inputs charged per tile
- Realtime audio API priced separately (~$6 / 1M input audio tokens)

Key limitations: Video understanding remained limited. Image generation, while impressive, sometimes struggled with precise spatial layouts. The model family became complex to navigate (4o vs 4.5 vs o-series).

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Google DeepMind: Gemini 2.0 / 2.5

Gemini 1.5 Pro set the stage with its 1M+ token context window and native multimodal architecture. Gemini 2.0 Flash (December 2024) and subsequent 2.x models pushed further.

Gemini 2.0 Flash:
- Optimized for speed and cost, with native tool use, multimodal generation (text + images), and a massive context window.
- Introduced “agentic” capabilities with native code execution, Google Search grounding, and function calling.
- Supported native audio output (multilingual).

Gemini 2.5 Pro (March 2025) became one of the strongest models overall:
- Text: Set new state-of-the-art on several reasoning benchmarks. Topped the LMArena leaderboard upon release.
- Image understanding: Extremely strong. 1M+ token context meant it could ingest enormous visual documents. Led benchmarks on MMMU and visual reasoning tasks.
- Video understanding: A key differentiator. Native long-form video understanding — could process hours of video within its context window and answer detailed questions. This was arguably the strongest video capability of any foundation model.
- Audio/Speech: Native audio understanding. Could transcribe, reason about audio content, and perform multilingual speech tasks.
- Code: Topped coding benchmarks including SWE-bench. “Thinking” mode enabled deep chain-of-thought for complex code reasoning.
- Tool use: Native integration with Google Search, code execution sandbox, and structured function calling.
- Image generation: Gemini 2.0 Flash introduced native image generation; this capability continued through the 2.x line.

Pricing (Gemini 2.5 Pro, early 2025):
- Input: $1.25 / 1M tokens (under 200K); $2.50 (over 200K)
- Output: $10.00 / 1M tokens (non-thinking); thinking tokens priced lower
- Flash variants significantly cheaper ($0.15 / 1M input tokens)

Key limitations: Occasional verbosity in outputs. Some reports of inconsistency in instruction following compared to Claude/GPT on nuanced tasks. Google ecosystem lock-in for some features (Search grounding).

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Anthropic: Claude 3.5 Sonnet, Claude 3.5 Haiku, Claude 4 Opus/Sonnet

Claude 3.5 Sonnet (October 2024 updated version) was the workhorse model through late 2024 and early 2025, excelling at coding, analysis, and instruction following.

Claude 4 Opus and Claude 4 Sonnet (released May 2025) represented Anthropic’s next generation:
- Text: State-of-the-art on instruction following, nuance, and complex analysis. Claude 4 Opus was positioned as having the deepest reasoning capability. Strong MMLU, GPQA, and humanities benchmarks.
- Image understanding: Accepts images natively. Strong on document understanding, chart analysis, and visual reasoning. Competitive on MMMU. However, Claude’s vision was considered slightly behind Gemini on spatial/geometric tasks historically.
- Image generation: Not supported. Anthropic did not offer native image generation — this remained a notable gap compared to GPT-4o and Gemini.
- Video understanding: Not directly supported as of the Claude 4 launch. Images could be provided as individual frames, but there was no native video input.
- Audio/Speech: Not natively supported in the standard API. Anthropic had not shipped native audio input/output by the Claude 4 launch.
- Code: Extremely strong. Claude 4 Sonnet was widely regarded as one of the best coding models, particularly for agentic coding workflows (SWE-bench, real-world code generation). “Extended thinking” mode enabled deep reasoning.
- Tool use: Best-in-class tool use and agentic capabilities. Robust function calling, computer use (GUI interaction), MCP (Model Context Protocol) integration for connecting to external tools and data sources.

Pricing (Claude 4 Sonnet, May 2025):
- Input: $3.00 / 1M tokens; Output: $15.00 / 1M tokens
- Claude 4 Opus: $15.00 / 1M input; $75.00 / 1M output
- Haiku variants much cheaper

Key limitations: No image generation, no native audio, no native video understanding. These were significant gaps in multimodal coverage. Anthropic’s strength was depth of reasoning and safety rather than breadth of modalities.

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Meta: Llama Multimodal

Llama 3.2 (September 2024) was Meta’s entry into multimodal open-weight models:
- 11B and 90B parameter vision variants
- Text + image understanding (no generation)
- Competitive with GPT-4V on some vision benchmarks at the 90B scale
- Fully open-weight, enabling local deployment and fine-tuning

Llama 4 (April 2025) significantly expanded Meta’s ambitions:
- Llama 4 Scout (17B active params, 109B total via mixture-of-experts): 10M token context window — the largest of any model at launch. Natively multimodal (text + image).
- Llama 4 Maverick (17B active params, 400B total): Stronger reasoning, still natively multimodal.
- Both models used an early-fusion architecture with interleaved attention between text and vision tokens.
- Competitive with Gemini 2.0 Flash and GPT-4o on multimodal benchmarks while being open-weight.

Pricing: Open-weight / free to deploy. Hosted API pricing varied by provider (Together, Fireworks, etc.) but was generally among the cheapest options.

Key limitations: No image generation, no audio/speech natively. Multimodal capability limited to image understanding + text. Community fine-tunes expanded capability but were not official. Llama 4’s initial reception had some controversy around benchmark reporting methodology.

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Other Notable Models

Mistral: Released multimodal models (Pixtral series). Pixtral Large (124B) offered competitive vision-language capabilities. Mistral focused more on the European market and multilingual capabilities.

Qwen (Alibaba): Qwen2.5-VL and QwQ models were strong competitors, especially in Asian language benchmarks and multimodal understanding. The Qwen2.5 series was competitive with Llama at similar scales. Open-weight.

DeepSeek: DeepSeek-V3 and DeepSeek-R1 made waves for extremely cost-efficient training and strong reasoning. DeepSeek-R1 (January 2025) matched o1-level reasoning at a fraction of the cost. Primarily text-focused but with expanding multimodal capabilities.

Grok (xAI): Grok-3 (February 2025) was notable for DeepSearch and reasoning capabilities, integrated into the X platform. Multimodal capabilities expanded through image understanding.

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2. Capability Matrix

Capability	GPT-4o/o3	Gemini 2.5	Claude 4	Llama 4	DeepSeek R1
Text reasoning	Excellent	Excellent	Excellent	Very Good	Excellent
Image understanding	Excellent	Excellent	Very Good	Good	Limited
Image generation	Yes (native)	Yes (native)	No	No	No
Video understanding	Limited	Excellent	No	No	No
Audio in/out	Yes (native)	Yes (native)	No	No	No
Code generation	Excellent	Excellent	Excellent	Very Good	Excellent
Tool use / Agents	Excellent	Excellent	Excellent	Good	Moderate
Context window	128K	1M+	200K (1M Opus)	10M (Scout)	128K
Open-weight	No	No	No	Yes	Yes
Cost efficiency	Moderate	Good (Flash)	Moderate-High	Cheapest	Very Cheap

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3. Benchmark Snapshot (approximate, as of early-mid 2025)

Benchmark	GPT-4o	GPT-o3	Gemini 2.5 Pro	Claude 4 Sonnet	Llama 4 Maverick
MMLU	~88%	~92%	~90%	~89%	~85%
GPQA Diamond	~53%	~68%	~67%	~65%	~55%
HumanEval	~90%	~93%	~92%	~93%	~86%
SWE-bench Verified	~38%	~69%	~63%	~70%	~42%
MMMU	~69%	~74%	~75%	~70%	~64%
MathVista	~63%	~78%	~76%	~73%	~60%
MATH-500	~76%	~96%	~95%	~90%	~78%

Note: These numbers are approximate composites from published results through mid-2025. Benchmark conditions and exact versions vary across providers. Reasoning models (o3, Gemini thinking, Claude extended thinking) generally score significantly higher than their base counterparts.

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4. Key Trends and Analysis

Trend 1: Modality convergence — but uneven

The industry moved decisively toward omnimodal architectures. GPT-4o and Gemini led in breadth (text, image, audio, video). Anthropic and Meta prioritized depth in specific modalities (reasoning, code, tool use) over breadth. By early 2026, the question is not whether models are multimodal but how natively integrated those modalities are.

Trend 2: Reasoning models as a separate paradigm

The o1/o3 line from OpenAI, Gemini’s “thinking” mode, Claude’s “extended thinking,” and DeepSeek-R1 established chain-of-thought reasoning as a distinct capability layer. These models trade latency and cost for dramatically better performance on math, science, and complex code tasks.

Trend 3: The context window race

Gemini pushed to 1M+ tokens, Llama 4 Scout claimed 10M tokens. Massive context enabled new use cases (full codebase analysis, long video understanding, multi-document synthesis) but raised questions about effective attention at extreme lengths — “needle-in-a-haystack” performance often degraded beyond certain thresholds.

Trend 4: Open-weight competition intensified

Llama 4, Qwen 2.5, DeepSeek, and Mistral made strong open-weight models broadly available. The capability gap between open and closed models narrowed significantly, particularly for text and code tasks. Multimodal capability in open models still lagged behind frontier closed models.

Trend 5: Agentic capabilities became central

Tool use, function calling, computer use (GUI interaction), and multi-step task execution became key differentiators. Claude’s MCP protocol, OpenAI’s function calling, and Gemini’s native tool use all competed to define how models interact with external systems. This shifted evaluation from pure benchmarks toward real-world task completion.

Trend 6: Cost collapse

Prices dropped dramatically. GPT-4o-mini and Gemini Flash models offered near-frontier quality at 10-50x lower cost than the original GPT-4. DeepSeek demonstrated that frontier-level training was possible at dramatically lower compute budgets, pressuring the entire market.

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5. Practical Guidance by Use Case

Use Case	Recommended Model(s)	Rationale
General assistant	GPT-4o, Gemini 2.5 Flash	Broad modality, good balance
Deep analysis/research	Claude 4 Opus, GPT-o3	Deepest reasoning
Coding (agentic)	Claude 4 Sonnet, Gemini 2.5 Pro	SWE-bench leaders, tool use
Video analysis	Gemini 2.5 Pro	Only frontier model with native long video
Voice/audio apps	GPT-4o (Realtime API)	Most mature native audio
Image generation	GPT-4o, Gemini 2.0 Flash	Both have native generation
On-premise/private	Llama 4, Qwen 2.5	Open-weight, self-hostable
Budget-conscious	Gemini Flash, DeepSeek, Llama 4	Best cost/capability ratio
Safety-critical	Claude 4	Strongest alignment and refusal calibration

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6. Limitations and Caveats

1. Benchmark saturation: Top models cluster within a few percentage points on standard benchmarks. Real-world task performance, instruction following quality, and failure modes matter more than benchmark rankings for most applications.

2. Evaluation challenges: There is no widely accepted benchmark for multimodal generation quality, agentic task completion, or conversational naturalness. Published benchmarks favor text-heavy tasks.

3. Temporal note: My training data extends through May 2025. Developments after that date — including any GPT-5 release, further Claude 4 updates, Gemini 3.x, or Llama 5 — are not covered here. The landscape moves quickly; specific pricing and benchmark numbers should be verified against current data.

4. Video and audio remain immature: Even the leading models (Gemini for video, GPT-4o for audio) have significant limitations in these modalities compared to their text capabilities. True omnimodal parity has not been achieved.

5. Hallucination remains unsolved: All models still hallucinate, particularly on factual recall and citation tasks. Retrieval-augmented generation (RAG) and tool use mitigate but do not eliminate this issue.

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Summary

As of early 2026, the multimodal foundation model landscape is characterized by:

• Google Gemini 2.5 leads in modality breadth (text, image, audio, video, generation) and context length
• OpenAI GPT-4o / o-series leads in native audio interaction and has the most mature developer ecosystem
• Anthropic Claude 4 leads in coding, agentic tool use, and reasoning depth, but lacks image generation, audio, and video
• Meta Llama 4 leads the open-weight space with competitive multimodal understanding and unprecedented context windows
• DeepSeek disrupted pricing assumptions and proved frontier reasoning is achievable at dramatically lower cost

No single model dominates across all dimensions. The practical choice depends on which modalities and capabilities matter most for a given application.