Pick Grok 4.7 for 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price or deepswe v1.1 (high effort): 71.0%, up from grok 4.6's 65.2%; cursorbench 4.0: 46.3%, up from 40.4%. Pick Mistral NeMo for multilingual understanding across 11+ languages or runs on a single gpu with fp8 quantization-aware training. Choose Mistral NeMo if you need self-hosting or data privacy; Grok 4.7 if you want a managed API.
Grok 4.7 (xAI, US) and Mistral NeMo (Mistral, France) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Grok 4.7 is xAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks. Mistral NeMo is a 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: Mistral NeMo is about 100× cheaper on input ($0.02/$0.03 per 1M tokens vs $2/$6 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Grok 4.7 holds 3.8× more — 500K tokens (~750 pages) vs 128K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Grok 4.7 is the newer model by about 27 months (released September 21, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Grok 4.7
Mistral NeMo
Provider
xAI (US)
Mistral (France)
Released
September 21, 2026
July 18, 2024
Context window
500K tokens (~750 pages)
128K (~197 pages)
Price (in/out)
$2/$6 per 1M tokens
$0.02/$0.03 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, code
text
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
2.1 trillion parameters, up 40% from Grok 4.6's 1.5 trillion, at the same $2/$6 per million token price: Grok 4.7 — Its 500K tokens window holds about 3.8× more than Mistral NeMo's 128K in a single prompt.
DeepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4%: Grok 4.7 — XAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks — and it carries the larger 500K tokens context.
Trained with supplemental SpaceX data (Starlink telemetry, manufacturing records, engineering failure logs) — xAI says this improves reasoning about hardware and physical systems: Grok 4.7 — Mistral NeMo is comparatively weak here — 12B scale trails larger frontier models on complex reasoning and coding
Multilingual understanding across 11+ languages: Mistral NeMo — A 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU — and it runs cheaper at $0.02/$0.03 per 1M tokens.
Runs on a single GPU with FP8 quantization-aware training: Mistral NeMo — A 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU — and its weights are open while Grok 4.7 is API-only.
128K-token context for long documents: Mistral NeMo — Grok 4.7 is comparatively weak here — 500K context window trails several rivals now sitting at 1M+
Lowest cost at scale: Mistral NeMo — At $0.02/$0.03 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Grok 4.7 — Its 500K tokens window is about 3.8× larger than Mistral NeMo's 128K, fitting roughly 750 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Mistral NeMo — At $0.02/$0.03 per 1M tokens it undercuts Grok 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Grok 4.7 — Larger 500K tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs: Mistral NeMo — Open weights let you run it on your own hardware; Grok 4.7 is API-only.
Anyone whose priority is 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price: Grok 4.7 — It is specifically built for that.
Anyone whose priority is multilingual understanding across 11+ languages: Mistral NeMo — That is its strongest area.
An enterprise with regional data-residency rules: Grok 4.7 or Mistral NeMo — Origin (US vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Grok 4.7: where it fits
XAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks. Released September 21, 2026 by xAI, it is built for 2.1 trillion parameters, up 40% from Grok 4.6's 1.5 trillion, at the same $2/$6 per million token price, deepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4%, trained with supplemental SpaceX data (Starlink telemetry, manufacturing records, engineering failure logs) — xAI says this improves reasoning about hardware and physical systems, and xAI's strongest safety guardrails to date, per the company.
Its trade-offs are real: release was delayed at least five times since late July 2026 before shipping, 500K context window trails several rivals now sitting at 1M+, and reviewers note it arrives "late to the AI frontier party" against GPT-6 Astra, Claude Fable 5.1 and Opus 5.5, all shipped in the weeks just before it. At $2 in / $6 out per million tokens, it sits in the mid price band.
Mistral NeMo: where it fits
A 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. Released July 18, 2024 by Mistral, it is built for multilingual understanding across 11+ languages, runs on a single GPU with FP8 quantization-aware training, 128K-token context for long documents, and function calling and structured tool use.
Its trade-offs: discontinued - deprecated May 22, 2026 and fully retired July 31, 2026; replaced by Ministral 3 8B, 12B scale trails larger frontier models on complex reasoning and coding, and text-only; no vision or audio input. At $0.02 in / $0.03 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. Mistral NeMo gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Grok 4.7 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is Grok 4.7 or Mistral NeMo better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Grok 4.7 leans toward 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price while Mistral NeMo leans toward multilingual understanding across 11+ languages, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Grok 4.7 or Mistral NeMo?
Mistral NeMo is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Grok 4.7 is API-metered at $2/$6 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Grok 4.7 — 500K tokens vs 128K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Grok 4.7 and Mistral NeMo together?
Yes — a multi-model platform like LumiChats gives you Grok 4.7, Mistral NeMo and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Grok 4.7 or Mistral NeMo?
Grok 4.7 — released September 21, 2026, about 27 months after Mistral NeMo.
Grok 4.7 vs Mistral NeMo
xAI · US | Mistral · France · Updated June 2026
Quick verdict
Pick Grok 4.7 for 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price or deepswe v1.1 (high effort): 71.0%, up from grok 4.6's 65.2%; cursorbench 4.0: 46.3%, up from 40.4%. Pick Mistral NeMo for multilingual understanding across 11+ languages or runs on a single gpu with fp8 quantization-aware training. Choose Mistral NeMo if you need self-hosting or data privacy; Grok 4.7 if you want a managed API.
Grok 4.7 (xAI, US) and Mistral NeMo (Mistral, France) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Grok 4.7 is xAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks. Mistral NeMo is a 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Mistral NeMo is about 100× cheaper on input ($0.02/$0.03 per 1M tokens vs $2/$6 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Grok 4.7 holds 3.8× more — 500K tokens (~750 pages) vs 128K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Grok 4.7 is the newer model by about 27 months (released September 21, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Grok 4.7
Mistral NeMo
Provider
xAI (US)
Mistral (France)
Released
September 21, 2026
July 18, 2024
Context window
500K tokens (~750 pages)
128K (~197 pages)
Price (in/out)
$2/$6 per 1M tokens
$0.02/$0.03 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, code
text
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
2.1 trillion parameters, up 40% from Grok 4.6's 1.5 trillion, at the same $2/$6 per million token price
Grok 4.7
Its 500K tokens window holds about 3.8× more than Mistral NeMo's 128K in a single prompt.
DeepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4%
Grok 4.7
XAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks — and it carries the larger 500K tokens context.
Trained with supplemental SpaceX data (Starlink telemetry, manufacturing records, engineering failure logs) — xAI says this improves reasoning about hardware and physical systems
Grok 4.7
Mistral NeMo is comparatively weak here — 12B scale trails larger frontier models on complex reasoning and coding
Multilingual understanding across 11+ languages
Mistral NeMo
A 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU — and it runs cheaper at $0.02/$0.03 per 1M tokens.
Runs on a single GPU with FP8 quantization-aware training
Mistral NeMo
A 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU — and its weights are open while Grok 4.7 is API-only.
128K-token context for long documents
Mistral NeMo
Grok 4.7 is comparatively weak here — 500K context window trails several rivals now sitting at 1M+
Lowest cost at scale
Mistral NeMo
At $0.02/$0.03 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Grok 4.7
Its 500K tokens window is about 3.8× larger than Mistral NeMo's 128K, fitting roughly 750 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Mistral NeMo
At $0.02/$0.03 per 1M tokens it undercuts Grok 4.7, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Grok 4.7
Larger 500K tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Mistral NeMo
Open weights let you run it on your own hardware; Grok 4.7 is API-only.
Anyone whose priority is 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price
→ Grok 4.7
It is specifically built for that.
Anyone whose priority is multilingual understanding across 11+ languages
→ Mistral NeMo
That is its strongest area.
An enterprise with regional data-residency rules
→ Grok 4.7 or Mistral NeMo
Origin (US vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Grok 4.7: where it fits
XAI's September 21, 2026 update — a 2.1T-parameter model trained partly on SpaceX hardware data, same price as Grok 4.6 but stronger on coding benchmarks. Released September 21, 2026 by xAI, it is built for 2.1 trillion parameters, up 40% from Grok 4.6's 1.5 trillion, at the same $2/$6 per million token price, deepSWE v1.1 (high effort): 71.0%, up from Grok 4.6's 65.2%; CursorBench 4.0: 46.3%, up from 40.4%, trained with supplemental SpaceX data (Starlink telemetry, manufacturing records, engineering failure logs) — xAI says this improves reasoning about hardware and physical systems, and xAI's strongest safety guardrails to date, per the company.
Its trade-offs are real: release was delayed at least five times since late July 2026 before shipping, 500K context window trails several rivals now sitting at 1M+, and reviewers note it arrives "late to the AI frontier party" against GPT-6 Astra, Claude Fable 5.1 and Opus 5.5, all shipped in the weeks just before it. At $2 in / $6 out per million tokens, it sits in the mid price band.
Mistral NeMo: where it fits
A 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. Released July 18, 2024 by Mistral, it is built for multilingual understanding across 11+ languages, runs on a single GPU with FP8 quantization-aware training, 128K-token context for long documents, and function calling and structured tool use.
Its trade-offs: discontinued - deprecated May 22, 2026 and fully retired July 31, 2026; replaced by Ministral 3 8B, 12B scale trails larger frontier models on complex reasoning and coding, and text-only; no vision or audio input. At $0.02 in / $0.03 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. Mistral NeMo gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Grok 4.7 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Grok 4.7 and Mistral NeMo without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Grok 4.7 leans toward 2.1 trillion parameters, up 40% from grok 4.6's 1.5 trillion, at the same $2/$6 per million token price while Mistral NeMo leans toward multilingual understanding across 11+ languages, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Grok 4.7 or Mistral NeMo?
Mistral NeMo is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Grok 4.7 is API-metered at $2/$6 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Grok 4.7 — 500K tokens vs 128K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Grok 4.7 and Mistral NeMo together?
Yes — a multi-model platform like LumiChats gives you Grok 4.7, Mistral NeMo and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Grok 4.7 or Mistral NeMo?
Grok 4.7 — released September 21, 2026, about 27 months after Mistral NeMo.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.